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  library_name: transformers
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
 
 
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  ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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  ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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  ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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  ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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  ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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  ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training Details
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  ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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  #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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  ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
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  library_name: transformers
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+ license: mit
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+ datasets:
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+ - thibaud-perrin/hibo-function-calling-v1
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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  ---
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+ # Model Card for thibaud-perrin/hibo-mistral-7b-fc-v1.3
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+ <div align="center">
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+ <img src="./img/banner2.webp" width="100%" />
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+ </div>
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+ [![GitHub](https://img.shields.io/badge/GitHub-Repository-blue.svg)](https://github.com/thibaud-perrin/hibo-mistral-7b-fc)
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+ 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.
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+ ## Model Details
 
 
 
 
 
 
 
 
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+ ### Model Description
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+ 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.
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+ - **Developed by:** Thibaud Perrin
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+ - **Model type:** CAUSAL_LM
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+ - **Language(s) (NLP):** English
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+ - **License:** MIT
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+ - **Finetuned from model:** Mistral-7B
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  ## Uses
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+ 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.
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  ### Direct Use
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+ The model can be directly used via the Hugging Face Transformers library for generating text based on prompts related to instructions or function calls.
 
 
 
 
 
 
 
 
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  ### Out-of-Scope Use
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+ This model is not intended for high-stakes decisions or scenarios where misunderstanding instructions could lead to significant consequences.
 
 
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  ## Bias, Risks, and Limitations
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+ 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.
 
 
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  ### Recommendations
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+ 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.
 
 
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  ## How to Get Started with the Model
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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+
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+ model_identifier = "thibaud-perrin/hibo-mistral-7b-fc-v1.3"
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_identifier,
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+ low_cpu_mem_usage=True,
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+ return_dict=True,
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+ torch_dtype=torch.bfloat16,
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+ device_map={"": 0},
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained(model_identifier)
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+
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+ device = 'cuda:0'
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+ # device = 'cpu'
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+ model.config.use_cache = True
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+ model.eval()
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+ model.to(device)
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+
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+ def stream(user_prompt):
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+ system_prompt = """You are a helpful assistant with access to the following functions. Use them if required -
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+ {
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+ "name": "get_stock_price",
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+ "description": "Get the current stock price of a company",
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+ "parameters": {
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+ "type": "object",
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+ "properties": {
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+ "company_name": {
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+ "type": "string",
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+ "description": "The name of the company"
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+ },
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+ "exchange": {
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+ "type": "string",
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+ "description": "The stock exchange where the company is listed"
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+ }
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+ },
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+ "required": [
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+ "company_name",
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+ "exchange"
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+ ]
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+ }
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+ }
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+ """
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+ messages = [
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+ {"role": "system", "content": system_prompt},
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+ {"role": "user", "content": user_prompt.strip()}
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+ ]
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+
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+ transformed_data = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
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+ eos_token_id = tokenizer.eos_token_id
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+ inputs = tokenizer([transformed_data], return_tensors="pt", add_special_tokens=True).to(device)
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+ streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
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+ _ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, early_stopping=True)
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+ stream("Hi, can you tell me the current stock price of Apple on NASDAQ? ")
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+ ```
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  ## Training Details
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  ### Training Data
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+ The model was trained using the dataset `thibaud-perrin/hibo-function-calling-v1`, which consists of various instruction-following and function-calling examples.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  #### Summary
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+ The fine-tuned model demonstrates a significant improvement in understanding and generating instruction-based responses compared to the base Mistral-7B model.
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+ However this model has been trained, only on the first 50_000 rows of the dataset, with one epoch.
 
 
 
 
 
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  ## Environmental Impact
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+ - **Hardware Type:** A100 - 40GB
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+ - **Hours used:** 48H
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+ - **Cloud Provider:** Google Colab
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+ - **Compute Region:** France
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+ - **Carbon Emitted:** Estimates needed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## 📚 Citation
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+ Please cite this dataset using the following BibTeX entry:
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+ ```bibtex
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+ @misc{hibo-mistral-7b-fc-v1.3,
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+ author = Thibaud Perrin,
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+ title = hibo-mistral-7b-fc-v1.3: An instruct Model for Function Calling in Conversational AI,
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+ year = 2024,
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+ publisher = Hugging Face,
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+ }
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+ ```