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muhammed-afsal-p-m/
afsal_llm

Name: afsal_llm
Availability: InStock

text-generation-inference

endpoints_compatible

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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.

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.

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

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.

Getting Started

Basic Usage

python

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))