harshil30402

Cognius-mini-1B-v2

Model Details

Model Description

Cognius-mini-1B-v2 is Llama-3.2-1B-Instruct fine-tuned with LoRA on 1,044 curated (question, reasoning, self-reflection, answer) examples. The training format asks the model to think through a question, critique its own reasoning, and then answer — rather than answering directly. Evaluated against the same-size untrained base model on 42 held-out questions, judged pairwise by an LLM on a 5-criterion rubric, the fine-tuned model scored 40.8% higher on average (8.68/10 vs. 6.16/10 for the base model).

Developed by: harshil30402
Model type: Causal decoder-only LM, LoRA adapter over Llama-3.2-1B-Instruct
Language(s): English
License: MIT
Finetuned from model: meta-llama/Llama-3.2-1B-Instruct

Model Sources

Repository: https://huggingface.co/harshil30402/Cognius-mini-1B-v2

Uses

Direct Use

Short-form question answering where a brief, structured reasoning + self-reflection pass before the final answer is useful — e.g. as a lightweight local reasoning assistant. The model was trained on short, general-knowledge questions (science, history, concepts), not on math word problems or formal logic puzzles.

Out-of-Scope Use

Not evaluated for factual reliability on specialized/technical domains, multi-turn dialogue, or long-context tasks. Not suitable for use where a wrong or fabricated factual answer could cause harm — outputs are not fact-checked or grounded.

Training Details

Training Data

1,044 examples, each with four fields: task (the question), reasoning (a chain-of-thought), self_reflection (a critique/refinement of that reasoning), and answer (the final answer). Topics are general open-domain knowledge questions (e.g. "How does photosynthesis work?", "What is artificial intelligence?"), not benchmark math/logic sets.
Split into 835 train / 104 val / 105 test at the raw-example level, then reformatted into a Llama-3 chat template; after formatting, 820 train / 164 val examples were actually used for training.
Known limitation: the held-out test split was not fully de-duplicated against training data — roughly 29% of test questions share task text with a training example (see Evaluation). Treat the evaluation numbers below as directionally correct rather than a clean generalization measurement.

Training Procedure

Supervised fine-tuning with TRL's SFTTrainer and a completion-only loss mask (DataCollatorForCompletionOnlyLM, splitting on <|end_header_id|> so loss is only computed on the assistant's response, not the prompt).

Preprocessing

Tokenizer: Llama-3.2-1B-Instruct's tokenizer with an added <|pad|> pad token (right-padded); vocabulary resized to 128,264 (pad_to_multiple_of=8 alignment).
Chat template wraps the question, a <think>...</think> reasoning block, and a <self_reflection>...</self_reflection> block ahead of the final answer, with a system prompt instructing the model to "think and express meta cognition" before answering.
max_seq_length: 512 tokens.

Training Hyperparameters

Table with columns: Hyperparameter, Value
Hyperparameter	Value
LoRA rank (r)	32
LoRA alpha	16
LoRA dropout	0.005
LoRA target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
LoRA bias	none
Learning rate	1e-4
LR scheduler	constant, 10% warmup
Optimizer	AdamW (torch), β=(0.9, 0.999), ε=1e-8
Effective batch size

Speeds, Sizes, Times

Trained locally on Apple Silicon (MPS), not a cloud GPU. Train loss fell from 1.18 to 0.86 over the 102 steps.

Evaluation

Testing Data, Factors & Metrics

Testing data: 42 held-out questions (see the data-contamination caveat above).
Method: for each question, both the untrained base model (Llama-3.2-1B-Instruct) and this fine-tuned model generated one response. An LLM judge (openai/gpt-oss-120b, served via Groq) scored each response 0–10 against the ground-truth answer on five criteria — Accuracy, Completeness, Clarity & Coherence, Conciseness & Relevance, and Depth of Reasoning — and the five scores were averaged into one overall score per response.
Note on judge model: the original judge planned for this project (Llama-3.3-70B) was retired from Groq's API by the time this evaluation was run; openai/gpt-oss-120b (120B open-weight model) was used instead as the closest available substitute, with an identical rubric.

Results

Table with columns: Mean score (0–10)
	Mean score (0–10)
Base model (Llama-3.2-1B-Instruct, untrained)	6.16
Cognius-mini-1B-v2 (fine-tuned)	8.68
Relative improvement	+40.8%

Summary

The fine-tuned model consistently produced shorter, more on-target answers matching the ground-truth style, versus the base model's more verbose, sometimes tangential, and occasionally erroneous (e.g. false-refusal) responses. Given the test-set overlap noted above, treat the magnitude as an upper-bound estimate rather than a precise generalization number.

How to Get Started with the Model

python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_name = "meta-llama/Llama-3.2-1B-Instruct"
adapter_name = "harshil30402/Cognius-mini-1B-v2"

tokenizer = AutoTokenizer.from_pretrained(adapter_name)
base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
base_model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=8, mean_resizing=False)

model = PeftModel.from_pretrained(base_model, adapter_name)
model.eval()

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is artificial intelligence?"}],
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Bias, Risks, and Limitations

Evaluated on a small (42-example), partially train-overlapping test set — treat results as indicative, not a rigorous benchmark.
Judged by a single LLM judge rather than multiple independent judges or human raters; no inter-rater agreement was measured.
Trained on general-knowledge trivia-style questions; not evaluated on mathematical, logical, or multi-step reasoning benchmarks despite the "reasoning" framing.
Outputs are not fact-checked; the model can still produce incorrect or fabricated answers.

Technical Specifications

Compute Infrastructure

Hardware: Apple Silicon (MPS), local machine.
Software: PyTorch 2.6.0, Transformers 4.50.0, PEFT 0.15.0, TRL.

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Model Details

Model Provider

harshil30402

Model Tree

Base

meta-llama/Llama-3.2-1B-Instruct

Adapter

this model

Input Modalities

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Output Modalities

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Supported Functionality

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Model Details

Model Description

Developed by: harshil30402
Model type: Causal decoder-only LM, LoRA adapter over Llama-3.2-1B-Instruct
Language(s): English
License: MIT
Finetuned from model: meta-llama/Llama-3.2-1B-Instruct

Model Sources

Repository: https://huggingface.co/harshil30402/Cognius-mini-1B-v2

Uses

Direct Use

Out-of-Scope Use

Training Details

Training Data

1,044 examples, each with four fields: task (the question), reasoning (a chain-of-thought), self_reflection (a critique/refinement of that reasoning), and answer (the final answer). Topics are general open-domain knowledge questions (e.g. "How does photosynthesis work?", "What is artificial intelligence?"), not benchmark math/logic sets.
Split into 835 train / 104 val / 105 test at the raw-example level, then reformatted into a Llama-3 chat template; after formatting, 820 train / 164 val examples were actually used for training.
Known limitation: the held-out test split was not fully de-duplicated against training data — roughly 29% of test questions share task text with a training example (see Evaluation). Treat the evaluation numbers below as directionally correct rather than a clean generalization measurement.

Training Procedure

Preprocessing

Tokenizer: Llama-3.2-1B-Instruct's tokenizer with an added <|pad|> pad token (right-padded); vocabulary resized to 128,264 (pad_to_multiple_of=8 alignment).
Chat template wraps the question, a <think>...</think> reasoning block, and a <self_reflection>...</self_reflection> block ahead of the final answer, with a system prompt instructing the model to "think and express meta cognition" before answering.
max_seq_length: 512 tokens.

Training Hyperparameters

Table with columns: Hyperparameter, Value
Hyperparameter	Value
LoRA rank (r)	32
LoRA alpha	16
LoRA dropout	0.005
LoRA target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
LoRA bias	none
Learning rate	1e-4
LR scheduler	constant, 10% warmup
Optimizer	AdamW (torch), β=(0.9, 0.999), ε=1e-8
Effective batch size

Speeds, Sizes, Times

Trained locally on Apple Silicon (MPS), not a cloud GPU. Train loss fell from 1.18 to 0.86 over the 102 steps.

Evaluation

Testing Data, Factors & Metrics

Testing data: 42 held-out questions (see the data-contamination caveat above).
Method: for each question, both the untrained base model (Llama-3.2-1B-Instruct) and this fine-tuned model generated one response. An LLM judge (openai/gpt-oss-120b, served via Groq) scored each response 0–10 against the ground-truth answer on five criteria — Accuracy, Completeness, Clarity & Coherence, Conciseness & Relevance, and Depth of Reasoning — and the five scores were averaged into one overall score per response.
Note on judge model: the original judge planned for this project (Llama-3.3-70B) was retired from Groq's API by the time this evaluation was run; openai/gpt-oss-120b (120B open-weight model) was used instead as the closest available substitute, with an identical rubric.

Results

Table with columns: Mean score (0–10)
	Mean score (0–10)
Base model (Llama-3.2-1B-Instruct, untrained)	6.16
Cognius-mini-1B-v2 (fine-tuned)	8.68
Relative improvement	+40.8%

Summary

How to Get Started with the Model

python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_name = "meta-llama/Llama-3.2-1B-Instruct"
adapter_name = "harshil30402/Cognius-mini-1B-v2"

tokenizer = AutoTokenizer.from_pretrained(adapter_name)
base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
base_model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=8, mean_resizing=False)

model = PeftModel.from_pretrained(base_model, adapter_name)
model.eval()

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is artificial intelligence?"}],
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Bias, Risks, and Limitations

Evaluated on a small (42-example), partially train-overlapping test set — treat results as indicative, not a rigorous benchmark.
Judged by a single LLM judge rather than multiple independent judges or human raters; no inter-rater agreement was measured.
Trained on general-knowledge trivia-style questions; not evaluated on mathematical, logical, or multi-step reasoning benchmarks despite the "reasoning" framing.
Outputs are not fact-checked; the model can still produce incorrect or fabricated answers.

Cognius-mini-1B-v2

README

Model Details

Model Description

Model Sources

Uses

Direct Use

Out-of-Scope Use

Training Details

Training Data

Training Procedure

Preprocessing

Training Hyperparameters

Speeds, Sizes, Times

Evaluation

Testing Data, Factors & Metrics

Results

Summary

How to Get Started with the Model

Bias, Risks, and Limitations

Technical Specifications

Compute Infrastructure

Model Card Contact

Explore FriendliAI today

README

Model Details

Model Description

Model Sources

Uses

Direct Use

Out-of-Scope Use

Training Details

Training Data

Training Procedure

Preprocessing

Training Hyperparameters

Speeds, Sizes, Times

Evaluation

Testing Data, Factors & Metrics

Results

Summary

How to Get Started with the Model

Bias, Risks, and Limitations

Technical Specifications

Compute Infrastructure

Model Card Contact