Update README.md

README.md

@@ -1,21 +1,65 @@
 ---
-base_model: unsloth/Meta-Llama-3.1-8B-Instruct
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
 tags:
-- text-generation-inference
-- transformers
-- unsloth
+- function-calling
+- tool-use
+- fine-tuned
 - llama
-license: apache-2.0
-language:
-- en
+datasets:
+- Salesforce/xlam-function-calling-60k
+pipeline_tag: text-generation
 ---
 
-# Uploaded finetuned  model
+# Llama 3.1 8B Function Calling
 
-- **Developed by:** alfazick
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct
+Fine-tuned [Llama 3.1 8B Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) for function/tool calling.
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## Training
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+- **Dataset:** 900 examples from [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)
+- **Method:** LoRA (r=16, alpha=16)
+- **Trainable params:** 42M / 8B (0.52%)
+- **Epochs:** 1
+- **Loss:** 0.66 → 0.63
+
+## Evaluation (100 held-out samples)
+
+- **Exact match:** 62%
+- **Function name accuracy:** ~90%+
+
+## Usage
+```python
+from vllm import LLM
+
+llm = LLM(model="alfazick/llama-3.1-8b-function-calling")
+```
+
+Or with transformers:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("alfazick/llama-3.1-8b-function-calling")
+tokenizer = AutoTokenizer.from_pretrained("alfazick/llama-3.1-8b-function-calling")
+```
+
+## Prompt Format
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are a helpful assistant with access to the following tools or function calls. Your task is to produce a sequence of tools or function calls necessary to generate response to the user utterance. Use the following tools or function calls as required:
+[{"name": "func_name", "description": "...", "parameters": {...}}]<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{query}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+```
+
+## Output Format
+```json
+[{"name": "function_name", "arguments": {"arg": "value"}}]
+```
+
+## Limitations
+
+- Trained on 900 examples (proof of concept)
+- May have argument variations vs ground truth
+- Best for single/simple tool calls