Create README.md

README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+---
+# 🥷 Safurai-Csharp-34B
+
+📝 [Article](https://www.safurai.com/blog/introducing-safurai-csharp)
+📄 [Paper](https://www.safurai.com/)
+
+<center><img src="https://i.imgur.com/REPqbYM.png" width="300"></center>
+
+This is a [`codellama/CodeLlama-34b-hf`](https://huggingface.co/codellama/CodeLlama-34b-hf) model fine-tuned using QLoRA (4-bit precision) on 13B tokens of csharp evolved Q&A
+
+We obtained state-of-the-art performance on the MultiPL-E code LLM benchmark for csharp, reaching 56% at pass@1 with n=5.
+
+## 💻 Quantization
+
+This the AWQ quantized version of Safurai-Csharp-34B, it has been made by using the amazing [`AutoAWQ`](https://github.com/casper-hansen/AutoAWQ) library.
+
+## 🔧 Training
+
+It was trained on 2 x NVIDIA A100 PCIe 80GB in 7h 40m with the following configuration file:
+
+```yaml
+base_model: codellama/CodeLlama-34b-hf
+base_model_config: codellama/CodeLlama-34b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+hub_model_id: "Safurai/Evol-csharp-v1"
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: Safurai/EvolInstruct-csharp-16k-13B-Alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./qlora-out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project: codellama-csharp
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0003
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 40
+eval_steps: 40
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
+```
+
+## 📉 Training loss curve:
+
+![](https://i.imgur.com/rp1htuf.png)
+
+## 📊 Dataset composition:
+
+![](https://i.imgur.com/kTNXgGX.png)
+
+
+## 💻 Usage for AWQ
+
+``` python
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+
+quant_path = "Safurai/Safurai-Csharp-34B-AWQ"
+quant_file = "awq_model_w4_g128.pt"
+
+# Load model
+model = AutoAWQForCausalLM.from_quantized(quant_path, quant_file, fuse_layers=True)
+tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
+streamer = TextStreamer(tokenizer, skip_special_tokens=True)
+
+# Convert prompt to tokens
+prompt_template = """\
+A chat between a developer and an AI assistant. The assistant is an expert csharp programmer that can give useful and complete code responses.
+
+USER: {prompt}
+ASSISTANT:"""
+
+tokens = tokenizer(
+    prompt_template.format(prompt="How are you today?"), 
+    return_tensors='pt'
+).input_ids.cuda()
+
+# Generate output
+generation_output = model.generate(
+    tokens, 
+    streamer=streamer,
+    max_new_tokens=1024
+)
+```
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)