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linguai/
Jamba2-3B-Turkish-SFT-v1

Name: Jamba2-3B-Turkish-SFT-v1
Rating: 2.5 (137 reviews)

safetensors

jamba

turkish

instruction-following

lora-merged

text-generation

conversational

serda-dev/Jamba2-3B-Turkish

finetune

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Jamba2 3B Turkish SFT v1

This is the BF16 merged instruction-tuning artifact produced from the immutable Turkish CPT base serda-dev/Jamba2-3B-Turkish revision 9524af9e857e0359b8a3dad72bc216b65d3c0acd.

Training

Method: BF16 LoRA, merged into the base model.
Selected checkpoint: optimizer step 4,500 (not the final step).
SFT mixture: 35,800 reviewed/generated Turkish instruction examples.
Training run: 5,000 optimizer steps, 40,960,000 tokens.

Diagnostic evaluation

The isolated 84-case Gold suite is draft_quarantine, so these results are diagnostic rather than release-grade. The post-merge deterministic evaluation reproduced the selected checkpoint exactly:

Metric	Result
Semantic/task correctness	62/84
Contract compliance	58/84

gold.json, gold.md, merge_manifest.json, and SHA256SUMS accompany the weights for provenance and integrity checks. Final production promotion still requires the Gold-freeze, overlap/decontamination, and human-review gates.

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "linguai/Jamba2-3B-Turkish-SFT-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

Use the model according to the terms and conditions of the upstream base model and all included training sources.