Upload README.md

README.md

@@ -18,7 +18,7 @@ datasets:
 - togethercomputer/RedPajama-Data-1T
 model_name: OpenLLaMA 7B v2
 base_model: 
-- openlm-research/open_llama_7B_v2
+- openlm-research/open_llama_7b_v2
 inference: false
 model_creator: openlm-research
 pipeline_tag: text-generation
@@ -29,7 +29,7 @@ quantized_by: fbaldassarri
 
 ## Model Information
 
-Quantized version of [openlm-research/open_llama_7B_v2](https://huggingface.co/openlm-research/open_llama_7B_v2) using torch.float32 for quantization tuning.
+Quantized version of [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2) using torch.float32 for quantization tuning.
 - 4 bits (INT4)
 - group size = 128
 - Asymmetrical Quantization
@@ -39,7 +39,7 @@ Fast and low memory, 2-3X speedup (slight accuracy drop at W4G128)
 
 Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.6
 
-Note: this INT4 version of open_llama_7B_v2 has been quantized to run inference through CPU.
+Note: this INT4 version of open_llama_7b_v2 has been quantized to run inference through CPU.
 
 ## Replication Recipe
 
@@ -64,14 +64,14 @@ pip install -vvv --no-build-isolation -e .[cpu]
 
 ```
   from transformers import AutoModelForCausalLM, AutoTokenizer
-  model_name = "openlm-research/open_llama_7B_v2"
+  model_name = "openlm-research/open_llama_7b_v2"
   model = AutoModelForCausalLM.from_pretrained(model_name)
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   from auto_round import AutoRound
   bits, group_size, sym, device, amp = 4, 128, False, 'cpu', False
   autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
   autoround.quantize()
-  output_dir = "./AutoRound/openlm-research_open_llama_7B_v2-autoround-int4-gs128-asym"
+  output_dir = "./AutoRound/openlm-research_open_llama_7b_v2-autoround-int4-gs128-asym"
   autoround.save_quantized(output_dir, format='auto_round', inplace=True)
 ```