thibaud-perrin
/

hibo-mistral-7b-fc-v1.3

@@ -1,201 +1,143 @@
 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+license: mit
+datasets:
+- thibaud-perrin/hibo-function-calling-v1
+language:
+- en
+pipeline_tag: text-generation
 ---
+# Model Card for thibaud-perrin/hibo-mistral-7b-fc-v1.3
+<div align="center">
+    <img src="./img/banner2.webp" width="100%" />
+</div>
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue.svg)](https://github.com/thibaud-perrin/hibo-mistral-7b-fc)
+This model is a fine-tuned version of the `mistralai/Mistral-7B-v0.1` for the purpose of instruction following and function calling tasks. It is designed to understand and generate responses based on given instructions or function calls.
+## Model Details
+### Model Description
+Developed by Thibaud Perrin, this model is fine-tuned specifically for the task of interpreting instructions and generating appropriate responses or function calls in English. It leverages the power of the Mistral-7B model, adapting its capabilities to more targeted use cases.
+- **Developed by:** Thibaud Perrin
+- **Model type:** CAUSAL_LM
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** Mistral-7B
 ## Uses
+This model is intended for developers, researchers, and hobbyists looking for a pre-trained model capable of understanding and responding to instructions or executing function calls within a given context.
 ### Direct Use
+The model can be directly used via the Hugging Face Transformers library for generating text based on prompts related to instructions or function calls.
 ### Out-of-Scope Use
+This model is not intended for high-stakes decisions or scenarios where misunderstanding instructions could lead to significant consequences.
 ## Bias, Risks, and Limitations
+As with any language model, there's a risk of generating biased or inappropriate content. Users should be cautious and evaluate the model's outputs within their specific context.
 ### Recommendations
+Users should monitor the model's outputs and apply additional filtering or moderation as needed to ensure the generated content is appropriate for their use case.
 ## How to Get Started with the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+model_identifier = "thibaud-perrin/hibo-mistral-7b-fc-v1.3"
+model = AutoModelForCausalLM.from_pretrained(
+    model_identifier,
+    low_cpu_mem_usage=True,
+    return_dict=True,
+    torch_dtype=torch.bfloat16,
+    device_map={"": 0},
+)
+tokenizer = AutoTokenizer.from_pretrained(model_identifier)
+device = 'cuda:0'
+# device = 'cpu'
+model.config.use_cache = True
+model.eval()
+model.to(device)
+def stream(user_prompt):
+    system_prompt = """You are a helpful assistant with access to the following functions. Use them if required -
+    {
+        "name": "get_stock_price",
+        "description": "Get the current stock price of a company",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "company_name": {
+                    "type": "string",
+                    "description": "The name of the company"
+                },
+                "exchange": {
+                    "type": "string",
+                    "description": "The stock exchange where the company is listed"
+                }
+            },
+            "required": [
+                "company_name",
+                "exchange"
+            ]
+        }
+    }
+    """
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt.strip()}
+    ]
+    transformed_data = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+    eos_token_id = tokenizer.eos_token_id
+    inputs = tokenizer([transformed_data], return_tensors="pt", add_special_tokens=True).to(device)
+    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
+    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, early_stopping=True)
+stream("Hi, can you tell me the current stock price of Apple on NASDAQ? ")
+```
 ## Training Details
 ### Training Data
+The model was trained using the dataset `thibaud-perrin/hibo-function-calling-v1`, which consists of various instruction-following and function-calling examples.
 #### Summary
+The fine-tuned model demonstrates a significant improvement in understanding and generating instruction-based responses compared to the base Mistral-7B model.
+However this model has been trained, only on the first 50_000 rows of the dataset, with one epoch.
 ## Environmental Impact
+- **Hardware Type:** A100 - 40GB
+- **Hours used:** 48H
+- **Cloud Provider:** Google Colab
+- **Compute Region:** France
+- **Carbon Emitted:** Estimates needed
+## 📚 Citation
+Please cite this dataset using the following BibTeX entry:
+```bibtex
+@misc{hibo-mistral-7b-fc-v1.3,
+  author = Thibaud Perrin,
+  title = hibo-mistral-7b-fc-v1.3: An instruct Model for Function Calling in Conversational AI,
+  year = 2024,
+  publisher = Hugging Face,
+}
+```