Update Readme

README.md

@@ -1,209 +1,186 @@
 ---
+license: llama3
+language:
+- en
+library_name: transformers
+tags:
+- llama-3
+- llama-3.2
+- bitcoin
+- finance
+- instruction-following
+- fine-tuning
+- merged
 base_model: meta-llama/Llama-3.2-3B-Instruct
-library_name: peft
+datasets:
+- tahamajs/bitcoin-llm-finetuning-dataset
 pipeline_tag: text-generation
-tags:
-- base_model:adapter:meta-llama/Llama-3.2-3B-Instruct
-- lora
-- sft
-- transformers
-- trl
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Llama-3.2-3B-Instruct-Bitcoin-Analyst-v2
 
+This repository contains a specialized version of `meta-llama/Llama-3.2-3B-Instruct`, expertly fine-tuned to function as a **Bitcoin and cryptocurrency market analyst**. The model is the result of a multi-stage "continuation training" process, making it highly capable of understanding and responding to complex instructions in the financial domain.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
+This model is a Causal Language Model (CLM) based on the Llama 3.2 3B Instruct architecture. It was developed through a sequential fine-tuning process to enhance its knowledge and instruction-following capabilities for topics related to Bitcoin, blockchain technology, and financial markets.
 
+The training procedure involved two main stages:
+1.  **Initial Specialization:** The base model was first merged with a high-performing LoRA adapter (`tahamajs/llama-3.2-3b-instruct-bitcoin-analyst-perfect`) to provide a strong foundation of domain-specific knowledge.
+2.  **Continuation Training:** A new LoRA adapter was then trained on top of this already-specialized model using the `tahamajs/bitcoin-llm-finetuning-dataset`.
+3.  **Final Merge:** The final model available here is the result of merging the second adapter, combining the knowledge from all stages into a single, powerful model.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** tahamajs
+- **Model type:** Causal Language Model, Instruction-Tuned
+- **Language(s) (NLP):** English (en)
+- **License:** Llama 3 Community License Agreement
+- **Finetuned from model:** `meta-llama/Llama-3.2-3B-Instruct`
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** `tahamajs/llama-3.2-3b-instruct-bitcoin-analyst-perfect_v2`
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+This model is intended for direct use as an instruction-following chatbot for topics related to Bitcoin and cryptocurrency. It can be used for question answering, analysis, and explanation of complex financial and technical concepts. For best results, prompts should be formatted using the Llama 3 chat template.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is **not a financial advisor**. It should not be used for making investment decisions. The model's knowledge is limited to its training data and it may produce inaccurate or outdated information. It is not designed for general-purpose conversation outside of its specialized domain.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This model inherits the limitations of the base Llama 3.2 model and the biases present in its training data. In the financial domain, there is a risk of generating overly optimistic or pessimistic statements that could be misinterpreted as financial advice. Users should be aware of these risks and verify any factual information independently.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should critically evaluate all outputs from this model, especially when they pertain to financial metrics or price predictions. We recommend clearly stating to any end-users that the text is generated by an AI and is not a substitute for professional financial advice.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+Use the code below to load and run the model using the `transformers` library.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Use the ID of this repository
+model_id = "tahamajs/llama-3.2-3b-instruct-bitcoin-analyst-perfect_v2"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+# Use the Llama 3 chat template for instruction-following
+messages = [
+    {"role": "user", "content": "What is the Bitcoin halving and what is its expected impact on the price?"},
+]
+
+# Apply the chat template and tokenize
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+# Generate a response
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+
+# Decode and print the output
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+````
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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. -->
-
-[More Information Needed]
+The second stage of fine-tuning was performed on the `tahamajs/bitcoin-llm-finetuning-dataset`. This dataset contains instruction-response pairs related to Bitcoin, market analysis, and blockchain technology.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing
 
-#### Preprocessing [optional]
+The training data was formatted into the Llama 3 chat template using the following structure for each example:
 
-[More Information Needed]
+```
+<|begin_of_text|>
+<|start_header_id|>user<|end_header_id|>
+{instruction}
+{input}
+<|eot_id|>
+<|start_header_id|>assistant<|end_header_id|>
+{output}
+<|eot_id|>
+```
 
+The loss was calculated only on the assistant's response tokens.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+  - **Training regime:** `bf16` mixed precision with QLoRA
+  - **LoRA `r`:** 16
+  - **LoRA `alpha`:** 32
+  - **LoRA `dropout`:** 0.1
+  - **LoRA `target_modules`:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+  - **`learning_rate`:** 1e-4
+  - **`num_train_epochs`:** 1
+  - **`per_device_train_batch_size`:** 1
+  - **`gradient_accumulation_steps`:** 8
+  - **`optimizer`:** paged\_adamw\_32bit
+  - **`lr_scheduler_type`:** cosine
 
-#### Testing Data
+#### Training Loss
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The training loss shows a clear downward trend, indicating that the model successfully learned from the new data.
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+  - **Hardware Type:** Not specified
+  - **Hours used:** Not specified
+  - **Cloud Provider:** Not specified
+  - **Compute Region:** Not specified
+  - **Carbon Emitted:** Not estimated
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+This is a decoder-only transformer based on the Llama 3.2 architecture. It was fine-tuned using a causal language modeling objective.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+  - [PyTorch](https://pytorch.org/)
+  - [Transformers](https://github.com/huggingface/transformers)
+  - [PEFT](https://github.com/huggingface/peft) (v0.17.0)
+  - [TRL](https://github.com/huggingface/trl)
+  - [BitsAndBytes](https://github.com/TimDettmers/bitsandbytes)
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+tahamajs
 
 ## Model Card Contact
 
 [More Information Needed]
+
 ### Framework versions
 
-- PEFT 0.17.0
+  - PEFT 0.17.0
+