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Model: EpistemeAI/Fireball-R1.1-Llama-3.1-8B Source: Original Platform
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README.md
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README.md
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---
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base_model: EpistemeAI/Fireball-R1-Llama-3.1-8B
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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license: llama3.1
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language:
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- en
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---
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## Model Information
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# Fireball-R1.1-LLama-3.1-8B
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This is a state-of-the-art language model optimized for **neutrality**, **STEM proficiency**, and **ethical alignment**. Fine-tuned Deepseek-R1-distill-llama-8b-unsloth-bnb-4bit for science, chemistry, and mathematics with reduced cultural/political bias.
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This large language model is open source.
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---
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## Table of Contents
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- [Features](#features)
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- [Installation](#installation)
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- [Usage](#usage)
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- [Training Details](#training-details)
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- [Ethical Considerations](#ethical-considerations)
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- [License](#license)
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- [Citation](#citation)
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- [Contact](#contact)
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---
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## Features
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- **Neutral Worldview**: Minimizes political/cultural bias via globally diverse training data and human feedback.
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- **STEM Specialization**: Enhanced performance in:
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- **Chemistry**: Reaction mechanisms, periodic trends, spectroscopy.
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- **Mathematics**: Equation solving, proofs, calculus.
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- **General Science**: Hypothesis generation, research summarization.
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- **Ethical Guardrails**: Filters sensitive content and flags uncertain outputs.
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---
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## Installation
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```bash
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pip install transformers torch
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pip install accelerate
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pip install -U transformers
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```
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### Basic Inference
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```bash
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-R1.1-Llama-3.1-8B")
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model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Fireball-R1.1-Llama-3.1-8B")
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prompt = "Calculate the molar mass of sulfuric acid (H₂SO₄)."
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=200)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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##advance inference 8bit
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Load the tokenizer
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tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-R1.1-Llama-3.1-8B")
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# Load the model in 8-bit precision using bitsandbytes (requires a CUDA GPU)
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model = AutoModelForCausalLM.from_pretrained(
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"EpistemeAI/Fireball-R1.1-Llama-3.1-8B",
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load_in_8bit=True, # Enable 8-bit loading to reduce memory usage
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device_map="auto" # Automatically map model layers to the available device(s)
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)
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# Define the system prompt and the user prompt
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system_prompt = "You are a highly knowledgeable assistant with expertise in chemistry and physics. <think>"
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user_prompt = "Calculate the molar mass of sulfuric acid (H₂SO₄)."
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# Combine the system prompt with the user prompt. The format here follows a common convention for chat-like interactions.
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full_prompt = f"System: {system_prompt}\nUser: {user_prompt}\nAssistant:"
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# Tokenize the combined prompt and move the inputs to the GPU
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inputs = tokenizer(full_prompt, return_tensors="pt").to("cuda")
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# Generate output text from the model
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outputs = model.generate(**inputs, max_length=12200)
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# Decode and print the result, skipping special tokens
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result = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(result)
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## inference with 4bit:
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
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import torch
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# Define the quantization configuration for 4-bit mode.
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quant_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4", # Alternative: "fp4" (choose based on your model)
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bnb_4bit_compute_dtype=torch.float16, # Use FP16 for computations
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bnb_4bit_use_double_quant=True, # Optionally enable double quantization for better accuracy
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)
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# Load the tokenizer.
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tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Fireball-R1.1-Llama-3.1-8B")
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# Load the model with the 4-bit quantization configuration.
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model = AutoModelForCausalLM.from_pretrained(
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"EpistemeAI/Fireball-R1.1-Llama-3.1-8B",
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quantization_config=quant_config,
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device_map="auto" # Automatically assigns model parts to available devices
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)
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# Create a text-generation pipeline.
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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# Provide a text prompt and generate output.
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prompt = "How does the location of the Sydney Conservatorium of Music impact the academic and professional opportunities available to music students, and how does the conservatorium support student engagement with the music industry in Australia? output<think>"
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output = pipe(prompt)
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print(output)
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```
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### Recommended Parameters
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```bash
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outputs = model.generate(
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**inputs,
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max_length=300,
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temperature=0.7,
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top_p=0.95,
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repetition_penalty=1.2
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)
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```
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# Uploaded model
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- **Developed by:** EpistemeAI
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
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# Ethical Considerations
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Do Not Use For:
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- Medical/legal advice without expert oversight.
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- Generating partisan or culturally insensitive content.
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## Limitations:
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- May occasionally produce plausible but incorrect scientific explanations.
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- Not fully immune to subtle biases.
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## Thank you
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We appreciate the companies as following: Unsloth, Meta and Deepseek.
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## License
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This model is licensed under [apache-2.0] - see LICENSE for details.
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## Citation
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```
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@misc{Fireball-R1-Llama-3.1-8B,
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author = {EpistemeAI},
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title = {Fireball-R1-8B: A Neutral, Science-Optimized Language Model},
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year = {2025},
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url = {https://huggingface.co/EpistemeAI/Fireball-R1-Llama-3.1-8B}
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}
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```
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For support or feedback: contact us at episteme.ai@proton.me
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# Uploaded model
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- **Developed by:** EpistemeAI
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- **License:** apache-2.0
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- **Finetuned from model :** EpistemeAI/Fireball-R1-Llama-3.1-8B
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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config.json
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
|
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.48.3",
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"unsloth_fixed": true,
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"unsloth_version": "2025.2.15",
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"use_cache": true,
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"vocab_size": 128256
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}
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2072
tokenizer_config.json
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File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user