Parameter	Value
Batch Size	4
Gradient Accumulation Steps	1
Effective Batch Size	4
Learning Rate	1e-6
Number of Epochs	3
Max Prompt Length	800
Max Completion Length	700
Num Generations	8
Temperature	1.0
Beta (KL coefficient)	0.04

LoRA Configuration

Parameter	Value
LoRA Rank (r)	32
LoRA Alpha	64
LoRA Dropout	0.05

Training Settings

Parameter	Value
Logging Steps	1
Save Steps	100

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "kiranpg/Qwen2.5-OCamler-1.5B-Instruct-v2"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [
    {"role": "user", "content": "Write an OCaml function to compute the factorial of a number."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Use

This model is designed for generating OCaml code solutions given natural language problem descriptions. It has been fine-tuned on OCaml programming problems to improve its ability to produce correct, idiomatic OCaml code.

Limitations

The model may not always produce syntactically correct OCaml code
Complex algorithmic problems may require multiple attempts
The model works best with clear, well-specified problem descriptions

Training Infrastructure

Trained using TRL's GRPOTrainer with LoRA adapters.