Llama-base-3.1-8B-invoice-gguf-sft

afsal_llm

A custom language model built on top of Llama-3.1-8B, provided in SAFETENSORS format.
Designed for general text generation, conversational tasks, and experimentation with custom LLM pipelines.

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Model Details

Model Description

This model is a decoder-only transformer derived from Llama-3.1-8B, adapted for general-purpose text generation.

• Developed by: muhammed-afsal-p-m
• Base model: Llama-3.1-8B
• Model type: Auto-regressive language model
• Format: Multiple-shard SAFETENSORS files
• Included assets: tokenizer, config, generation config, chat template
• License: Specify license (e.g., MIT / Apache-2.0 / open source / other)

Model Sources

• Hugging Face Repository:
  https://huggingface.co/muhammed-afsal-p-m/afsal_llm

• Base Model Reference:
  Llama-3.1-8B (Meta)

• Chat Template:
  chat_template.jinja — supports structured conversational prompting.

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Intended Uses

Direct Use

• Free-form text generation
• Conversation and chatbot scenarios
• Research and prototyping with LLMs
• Prompt engineering experiments
• Offline / local inference with safetensors weights

Downstream Use

• Additional fine-tuning for specialized domains
• Integration into custom applications
• Evaluation benchmarks or comparative analysis

Out-of-Scope Use

Not recommended for high-risk domains without human oversight:

• Medical, legal, or financial advice
• Safety-critical decisions
• Factual tasks requiring guaranteed accuracy

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Risks, Biases & Limitations

• May generate incorrect, biased, or unsupported statements.
• Should not be treated as a factual source.
• Behavior outside the training distribution may be unpredictable.
• Outputs can vary depending on prompting and decoding strategy.

Recommendation: All outputs should be reviewed by a human in any production workflow.

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Getting Started

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "muhammed-afsal-p-m/afsal_llm"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True
)

prompt = "Hello, can you tell me something interesting?"
inputs = tokenizer(prompt, return_tensors="pt")

output = model.generate(
    inputs.input_ids,
    max_new_tokens=150,
)

print(tokenizer.decode(output[0], skip_special_tokens=True))