diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..52373fe24473b1aa44333d318f578ae6bf04b49b 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..22cbc7e81a6769ad67b6814ec1203466fd12d2d0 --- /dev/null +++ b/README.md @@ -0,0 +1,253 @@ +--- +license: llama4 +library_name: transformers +base_model: +- meta-llama/Llama-4-Scout-17B-16E +--- + +

+ Logo +

+ + +# Cogito v2 preview - 109B MoE + +[Blog Post](https://www.deepcogito.com/research/cogito-v2-preview) + +The Cogito v2 LLMs are instruction tuned generative models. All models are released under an open license for commercial use. + +- Cogito v2 models are hybrid reasoning models. Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning models). +- The LLMs are trained using **Iterated Distillation and Amplification (IDA)** - an scalable and efficient alignment strategy for superintelligence using iterative self-improvement. +- The models have been optimized for coding, STEM, instruction following and general helpfulness, and have significantly higher multilingual, coding and tool calling capabilities than size equivalent counterparts. + - In both standard and reasoning modes, Cogito v2-preview models outperform their size equivalent counterparts on common industry benchmarks. +- This model is trained in over 30 languages and supports long contexts (upto 10M tokens). + +# Evaluations +For detailed evaluations, please refer to the [Blog Post](https://www.deepcogito.com/research/cogito-v2-preview). + + +# Usage +Here is a snippet below for usage with Transformers: + +```python +import transformers +import torch + +model_id = "deepcogito/cogito-v2-preview-llama-109B-MoE" + +pipeline = transformers.pipeline( + "text-generation", + model=model_id, + model_kwargs={"torch_dtype": torch.bfloat16}, + device_map="auto", +) + +messages = [ + {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, + {"role": "user", "content": "Give me a short introduction to LLMs."}, +] + +outputs = pipeline( + messages, + max_new_tokens=512, +) + +print(outputs[0]["generated_text"][-1]) +``` + + + +## Implementing extended thinking +- By default, the model will answer in the standard mode. +- To enable thinking, you can do any one of the two methods: + - Set `enable_thinking=True` while applying the chat template. + - Add a specific system prompt, along with prefilling the response with "\\n". + +**NOTE: Unlike Cogito v1 models, we initiate the response with "\\n" at the beginning of every output when reasoning is enabled. This is because hybrid models can be brittle at times (<0.1% of the cases), and adding a "\\n" ensures that the model does indeed respect thinking.** + +### Method 1 - Set enable_thinking=True in the tokenizer +If you are using Huggingface tokenizers, then you can simply use add the argument `enable_thinking=True` to the tokenization (this option is added to the chat template). + +Here is an example - +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "deepcogito/cogito-v2-preview-llama-109B-MoE" + +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +prompt = "Give me a short introduction to LLMs." +messages = [ + {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, + {"role": "user", "content": prompt} +] + +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + enable_thinking=True +) +model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + +generated_ids = model.generate( + **model_inputs, + max_new_tokens=512 +) +generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) +] + +response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] +print(response) +``` + +### Method 2 - Add a specific system prompt, along with prefilling the response with "\\n". +To enable thinking using this method, you need to do two parts - + + +Step 1 - Simply use this in the system prompt `system_instruction = 'Enable deep thinking subroutine.'` + +If you already have a system_instruction, then use `system_instruction = 'Enable deep thinking subroutine.' + '\n\n' + system_instruction`. + +Step 2 - Prefil the response with the tokens `"\n"`. + +Here is an example - + +```python +import transformers +import torch + +model_name = "deepcogito/cogito-v2-preview-llama-109B-MoE" + +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +# Step 1 - Add deep thinking instruction. +DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine." + +messages = [ + {"role": "system", "content": DEEP_THINKING_INSTRUCTION}, + {"role": "user", "content": "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."}, +] + +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) + +# Step 2 - Prefill response with "\n". +text += "\n" + +# Now, continue as usual. +model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + +generated_ids = model.generate( + **model_inputs, + max_new_tokens=512 +) +generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) +] + +response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] +print(response) +``` + + +Similarly, if you have a system prompt, you can append the `DEEP_THINKING_INSTRUCTION` to the beginning in this way - + +```python +DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine." + +system_prompt = "Reply to each prompt with only the actual code - no explanations." +prompt = "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format." + +messages = [ + {"role": "system", "content": DEEP_THINKING_INSTRUCTION + '\n\n' + system_prompt}, + {"role": "user", "content": prompt} +] +``` + + +# Tool Calling +Cogito models support tool calling (single, parallel, multiple and parallel_multiple) both in standard and extended thinking mode. + +Here is a snippet - + +```python +# First, define a tool +def get_current_temperature(location: str) -> float: + """ + Get the current temperature at a location. + + Args: + location: The location to get the temperature for, in the format "City, Country" + Returns: + The current temperature at the specified location in the specified units, as a float. + """ + return 22. # A real function should probably actually get the temperature! + +# Next, create a chat and apply the chat template +messages = [ + {"role": "user", "content": "Hey, what's the temperature in Paris right now?"} +] + +model_inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True) + +text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False) +inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device) +outputs = model.generate(**inputs, max_new_tokens=512) +output_text = tokenizer.batch_decode(outputs)[0][len(text):] +print(output_text) +``` + +This will result in the output - +``` + +{"name": "get_current_temperature", "arguments": {"location": "Paris, France"}} +<|eot|> +``` + +You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so: + +```python +tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}} +messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]}) +``` + +and then call the tool and append the result, with the `tool` role, like so: + +```python +messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"}) +``` + +After that, you can `generate()` again to let the model use the tool result in the chat: + +```python +text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False) +inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device) +outputs = model.generate(**inputs, max_new_tokens=512) +output_text = tokenizer.batch_decode(outputs)[0][len(text):] +``` + +This should result in the string - +``` +'The current temperature in Paris is 22.0 degrees.<|eot|>' +``` + +## License +This repository and the model weights are licensed under the [Llama 4 Community License Agreement](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) (Llama models' default license agreement). + +## Contact +If you would like to reach out to our team, send an email to [contact@deepcogito.com](contact@deepcogito.com). \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..a8cbf971126cfee4ee39bb1e762b0767463d28c2 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,150 @@ +{{- bos_token }} +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} +{%- if not enable_thinking is defined %} + {%- set enable_thinking = false %} +{%- endif %} + +{#- This block extracts the system message, so we can slot it into the right place. #} +{%- if messages[0]['role'] == 'system' %} + {%- if messages[0]['content'] is string %} + {%- set system_message = messages[0]['content']|trim %} + {%- else %} + {%- set system_message = messages[0]['content'][0]['text']|trim %} + {%- endif %} + {%- set messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} +{%- endif %} + +{#- Set the system message. If enable_thinking is true, add the "Enable deep thinking subroutine." #} +{%- if enable_thinking %} + {%- if system_message != "" %} + {%- set system_message = "Enable deep thinking subroutine. + +" ~ system_message %} + {%- else %} + {%- set system_message = "Enable deep thinking subroutine." %} + {%- endif %} +{%- endif %} + +{#- System message + tools #} +{%- if tools is not none or system_message != '' %} + {{- "<|header_start|>system<|header_end|> + +" }} + {{- system_message }} + {%- if tools is not none %} + {%- if system_message != "" %} + {{- " + +" }} + {%- endif %} + {{- "Available Tools: +" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- " + +" }} + {%- endfor %} + {%- endif %} + {{- "<|eot|>" }} +{%- endif %} + +{#- Rest of the messages #} +{%- for message in messages %} + {#- Case 1 - Usual, non tool related message. #} + {%- if not (message.role == "ipython" or message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %} + {{- '<|header_start|>' + message['role'] + '<|header_end|> + +' }} + {%- if message['content'] is string %} + {{- message['content'] }} + {%- else %} + {%- for content in message['content'] %} + {%- if content['type'] == 'image' %} + {{- '<|image|>' }} + {%- elif content['type'] == 'text' %} + {{- content['text'] }} + {%- endif %} + {%- endfor %} + {%- endif %} + {{- "<|eot|>" }} + + {#- Case 2 - the response is from the assistant, but has a tool call returned. #} + {%- elif message.tool_calls is defined and message.tool_calls is not none %} + {{- "<|header_start|>assistant<|header_end|> + +" }} + {%- if message['content'] is string %} + {{- message['content'] }} + {%- if message['content'] | trim != "" %} + {{- " + +" }} + {%- endif %} + {%- else %} + {%- for content in message['content'] %} + {%- if content['type'] == 'image' %} + {{- '<|image|>' }} + {%- elif content['type'] == 'text' %} + {{- content['text'] }} + {%- if content['text'] | trim != "" %} + {{- " + +" }} + {%- endif %} + {%- endif %} + {%- endfor %} + {%- endif %} + {{- "[" }} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set out = tool_call.function|tojson %} + {%- if not tool_call.id is defined %} + {{- out }} + {%- else %} + {{- out[:-1] }} + {{- ', "id": "' + tool_call.id + '"}' }} + {%- endif %} + {%- else %} + {{- tool_call|tojson }} + {%- endif %} + {%- if not loop.last %} + {{- ", " }} + {%- else %} + {{- "]<|eot|>" }} + {%- endif %} + {%- endfor %} + + {#- Case 3 - the response is from a tool call. #} + {%- elif message.role == "ipython" or message["role"] == "tool_results" or message["role"] == "tool" %} + {{- "<|header_start|>ipython<|header_end|> + +" }} + {%- if message.tool_call_id is defined and message.tool_call_id != '' %} + {{- '{"content": ' }} + {%- if message.content is mapping or (message.content is iterable and not message.content is string) %} + {{- message.content | tojson }} + {%- else %} + {{- '"' ~ message.content ~ '"' }} + {%- endif %} + {{- ', "call_id": "' ~ message.tool_call_id ~ '"}' }} + {%- else %} + {%- if message.content is mapping or (message.content is iterable and not message.content is string) %} + {{- message.content | tojson }} + {%- else %} + {{- message.content }} + {%- endif %} + {%- endif %} + {{- "<|eot|>" }} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|header_start|>assistant<|header_end|>\n\n' }} + {%- if enable_thinking %} + {{- '\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..a4addce817558ca165db1f77f7cc6ce7ca24fa04 --- /dev/null +++ b/config.json @@ -0,0 +1,231 @@ +{ + "architectures": [ + "Llama4ForConditionalGeneration" + ], + "boi_token_index": 200080, + "eoi_token_index": 200081, + "image_token_index": 200092, + "model_type": "llama4", + "text_config": { + "attention_bias": false, + 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