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Model: brianknowsai/Brian-Llama-3.2-3B
Source: Original Platform

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README.md

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+---
+license: llama3.2
+metrics:
+- perplexity
+base_model:
+- meta-llama/Llama-3.2-3B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- Web3
+- Domain-Specific
+- NLP
+- Intent Recognition
+- Solidity
+language:
+- en
+---
+# Model Card for Brian-3B
+
+<img src="brian_llama2_logo.png" alt="Brian Logo" width="600"/>
+
+
+## Model Details
+
+### Model Description
+
+The **Brian-3B** model is a domain-specific language model tailored for Web3 applications. Built upon Meta’s Llama-3.2-3B, it is optimized for tasks involving natural language understanding and intent recognition in the blockchain ecosystem. 
+This includes tasks such as transaction intent parsing, Solidity code generation, and question answering on Web3-related topics.
+
+- **Developed by:** The Brian Team
+- **Funded by:** The Brian Team
+- **Shared by:** The Brian Team
+- **Model type:** Transformer-based autoregressive language model
+- **Language(s):** English
+- **License:** Llama 3.2 Community License
+- **Finetuned from:** meta-llama/Llama-3.2-3B
+
+**Please note**, this is just the first of a series of further training phases before the model can be used in production (estimated Q1 2025) to power our Intent Recognition Engine.
+The Brian team is calling on all partners interested in the space: developers, projects, and investors who might be involved in future phases of the model training.
+Join our [TG Dev chat](https://t.me/+NJjmAm2Y9p85Mzc0) if you have any questions or want to contribute to the model training. 
+
+### Model Sources
+
+- **Repository:** [Hugging Face Repository](https://huggingface.co/brianknowsai/Brian-Llama-3.2-3B)
+- **Demo:** This model will be integrated soon to power https://www.brianknows.org/
+- **Paper:** Coming soon
+
+## Uses
+
+### Downstream Use
+
+The model is specifically designed to be fine-tuned for downstream tasks such as:
+
+- **Transaction intent recognition**: Parsing natural language into JSON for transaction data.
+- **Solidity code generation**: Creating smart contracts based on user prompts.
+- **Web3 question answering**: Answering protocol-specific queries or extracting blockchain-related data.
+
+In the coming months, our team will release these task-specific models. 
+Anyone in the web3 space can fine-tune the model for other downstream tasks or improve its knowledge of specific ecosystems (e.g., Solana, Farcaster, etc.)
+
+### Out-of-Scope Use
+
+- Tasks outside the Web3 domain.
+- Generating harmful, unethical, or misleading content.
+
+## Bias, Risks, and Limitations
+
+### Recommendations
+
+While the model shows excellent performance in Web3-related domains, users should validate outputs for critical tasks like smart contract generation or 
+transaction execution to avoid errors. Fine-tuning is recommended for domain-specific applications.
+
+## How to Get Started with the Model
+
+To load and use the Brian-3B model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("brianknowsai/Brian-Llama-3.2-3B")
+tokenizer = AutoTokenizer.from_pretrained("brianknowsai/Brian-Llama-3.2-3B")
+
+# Generate text
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+input_text = "A web3 bridge is "
+
+# Tokenize the input text
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
+
+# Generate output (this is typical for causal language models)
+with torch.no_grad():
+    outputs = model.generate(input_ids, max_length=80, num_return_sequences=1)
+
+# Decode the generated tokens to text
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Print the result
+print(f"Input: {input_text}")
+print(f"Generated Brian text: {generated_text}")

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-3.2-3B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.47.1",
+  "use_cache": false,
+  "vocab_size": 128256
+}

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+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.47.1"
+}

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