> ## Documentation Index
> Fetch the complete documentation index at: https://docs.together.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Reasoning fine-tuning
> Train a reasoning model on chain-of-thought data.
Reasoning fine-tuning adapts a model that supports chain-of-thought reasoning. By providing `reasoning` or `reasoning_content` alongside the final assistant response, you shape how the model thinks through problems before producing an answer.
This page covers the reasoning data shape, supported models, and launch parameters.
Reasoning models should always be fine-tuned with reasoning data. Training a reasoning model without it can degrade its reasoning ability. If your dataset doesn't include reasoning, use an instruct model instead.
## Supported models
The following models support reasoning fine-tuning. See [supported models](/docs/fine-tuning/supported-models) for context lengths and batch limits.
| Organization | Model | API ID |
| ------------ | -------------------------------------------------- | ---------------------------------------------------- |
| NVIDIA | NVIDIA Nemotron 3 Nano Omni 30B A3B Reasoning BF16 | `nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16` |
| Qwen | Qwen3.5 397B A17B | `Qwen/Qwen3.5-397B-A17B` |
| Qwen | Qwen3.5 122B A10B | `Qwen/Qwen3.5-122B-A10B` |
| Qwen | Qwen3.5 35B A3B | `Qwen/Qwen3.5-35B-A3B` |
| Qwen | Qwen3.5 35B A3B Base | `Qwen/Qwen3.5-35B-A3B-Base` |
| Qwen | Qwen3.5 27B | `Qwen/Qwen3.5-27B` |
| Qwen | Qwen3.5 9B | `Qwen/Qwen3.5-9B` |
| Qwen | Qwen3.5 4B | `Qwen/Qwen3.5-4B` |
| Qwen | Qwen3.5 2B | `Qwen/Qwen3.5-2B` |
| Qwen | Qwen3.5 0.8B | `Qwen/Qwen3.5-0.8B` |
| Qwen | Qwen3.6 35B A3B | `Qwen/Qwen3.6-35B-A3B` |
| Qwen | Qwen3 Next 80B A3B Thinking | `Qwen/Qwen3-Next-80B-A3B-Thinking` |
| Qwen | Qwen3 0.6B | `Qwen/Qwen3-0.6B` |
| Qwen | Qwen3 0.6B Base | `Qwen/Qwen3-0.6B-Base` |
| Qwen | Qwen3 1.7B | `Qwen/Qwen3-1.7B` |
| Qwen | Qwen3 1.7B Base | `Qwen/Qwen3-1.7B-Base` |
| Qwen | Qwen3 4B | `Qwen/Qwen3-4B` |
| Qwen | Qwen3 4B Base | `Qwen/Qwen3-4B-Base` |
| Qwen | Qwen3 8B | `Qwen/Qwen3-8B` |
| Qwen | Qwen3 8B Base | `Qwen/Qwen3-8B-Base` |
| Qwen | Qwen3 14B | `Qwen/Qwen3-14B` |
| Qwen | Qwen3 14B Base | `Qwen/Qwen3-14B-Base` |
| Qwen | Qwen3 32B | `Qwen/Qwen3-32B` |
| Qwen | Qwen3 30B A3B Base | `Qwen/Qwen3-30B-A3B-Base` |
| Qwen | Qwen3 30B A3B | `Qwen/Qwen3-30B-A3B` |
| Qwen | Qwen3 235B A22B | `Qwen/Qwen3-235B-A22B` |
| Z.ai | GLM 5.1 | `zai-org/GLM-5.1` |
| Z.ai | GLM 5 | `zai-org/GLM-5` |
| Z.ai | GLM 4.7 | `zai-org/GLM-4.7` |
| Z.ai | GLM 4.6 | `zai-org/GLM-4.6` |
| OpenAI | GPT-OSS 20B | `openai/gpt-oss-20b` |
| OpenAI | GPT-OSS 120B | `openai/gpt-oss-120b` |
| Google | Gemma 4 31B IT | `google/gemma-4-31B-it` |
| Google | Gemma 4 31B IT VLM | `google/gemma-4-31B-it-VLM` |
| Google | Gemma 4 26B A4B IT | `google/gemma-4-26B-A4B-it` |
## Prepare your data
Prepare data in a JSONL file. Each assistant message should carry the chain of thought in a `reasoning` (or `reasoning_content`) field and the final answer in `content`.
### Conversational format
```json theme={null}
{
"messages": [
{"role": "user", "content": "What is the capital of France?"},
{
"role": "assistant",
"reasoning": "The user is asking about the capital of France. France is a country in Western Europe. Its capital city is Paris, which has been the capital since the 10th century.",
"content": "The capital of France is Paris."
}
]
}
```
When fine-tuning reasoning models on conversational data, only the last assistant message is trained on by default. For multi-turn reasoning, split the conversation so each assistant message is the final message in its own example.
### Preference format
For preference fine-tuning, both outputs carry `reasoning`. See [preference tuning](/docs/fine-tuning/preference-tuning) for the broader DPO workflow.
```json theme={null}
{
"input": {
"messages": [
{"role": "user", "content": "What is the capital of France?"}
]
},
"preferred_output": [
{
"role": "assistant",
"reasoning": "France is in Western Europe. Its capital is Paris.",
"content": "The capital of France is Paris."
}
],
"non_preferred_output": [
{
"role": "assistant",
"reasoning": "Let me think about European capitals.",
"content": "The capital of France is Berlin."
}
]
}
```
## Validate and upload
Upload your data using the Together Python/TypeScript SDK or the [Together CLI](/reference/cli/getting-started):
```python Python theme={null}
from together import Together
client = Together()
train_file = client.files.upload(
file="reasoning_dataset.jsonl",
purpose="fine-tune",
check=True,
)
print(train_file.id)
```
```typescript TypeScript theme={null}
import Together from "together-ai";
import fs from "node:fs";
const client = new Together();
const trainFile = await client.files.upload({
file: fs.createReadStream("reasoning_dataset.jsonl"),
purpose: "fine-tune",
});
console.log(trainFile.id);
```
```bash CLI theme={null}
tg files check "reasoning_dataset.jsonl"
tg files upload "reasoning_dataset.jsonl"
```
## Launch the job
LoRA is the default. Pass `lora=False` for full fine-tuning.
```python Python theme={null}
job = client.fine_tuning.create(
training_file=train_file.id,
model="Qwen/Qwen3-8B",
lora=True,
)
print(job.id)
```
```typescript TypeScript theme={null}
const job = await client.fineTuning.create({
training_file: trainFile.id,
model: "Qwen/Qwen3-8B",
lora: true,
});
console.log(job.id);
```
```bash CLI theme={null}
tg fine-tuning create \
--training-file "" \
--model "Qwen/Qwen3-8B" \
--lora
```
For details on every available parameter, see the [API reference](/reference/cli/finetune).
## Watch and deploy
Reasoning jobs use the same lifecycle as text jobs:
* [Poll the job](/docs/fine-tuning/monitoring#poll-until-the-job-is-done) with the SDK or CLI. Expect 10 to 30 minutes for a LoRA job on an 8B model with a few thousand examples.
* Deploy the result on a [dedicated endpoint](/docs/fine-tuning/deployment).
* Call the endpoint with the same chat-completions shape. The model emits `reasoning_content` alongside `content` for clients that surface it. See [Inference → Reasoning](/docs/inference/chat/reasoning) for details.