> ## 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.
# Function-calling fine-tuning
> Train a model to invoke tools and structured functions reliably.
Function-calling fine-tuning adapts a model to invoke tools in response to user queries. The result is a model that produces well-formed `tool_calls` with high reliability, useful for agents and any pipeline that depends on structured function invocation.
This page covers the function-calling data shape, supported models, and launch parameters.
## Supported models
The following models support function-calling 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` |
| NVIDIA | NVIDIA Nemotron Nano 9B v2 | `nvidia/NVIDIA-Nemotron-Nano-9B-v2` |
| NVIDIA | NVIDIA Nemotron 3 Super 120B A12B BF16 | `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-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 Instruct | `Qwen/Qwen3-Next-80B-A3B-Instruct` |
| Qwen | Qwen3 Next 80B A3B Thinking | `Qwen/Qwen3-Next-80B-A3B-Thinking` |
| Qwen | Qwen3 0.6B | `Qwen/Qwen3-0.6B` |
| Qwen | Qwen3 1.7B | `Qwen/Qwen3-1.7B` |
| Qwen | Qwen3 4B | `Qwen/Qwen3-4B` |
| Qwen | Qwen3 8B | `Qwen/Qwen3-8B` |
| Qwen | Qwen3 14B | `Qwen/Qwen3-14B` |
| Qwen | Qwen3 32B | `Qwen/Qwen3-32B` |
| Qwen | Qwen3 30B A3B | `Qwen/Qwen3-30B-A3B` |
| Qwen | Qwen3 30B A3B Instruct 2507 | `Qwen/Qwen3-30B-A3B-Instruct-2507` |
| Qwen | Qwen3 235B A22B | `Qwen/Qwen3-235B-A22B` |
| Qwen | Qwen3 235B A22B Instruct 2507 | `Qwen/Qwen3-235B-A22B-Instruct-2507` |
| Qwen | Qwen3 Coder 30B A3B Instruct | `Qwen/Qwen3-Coder-30B-A3B-Instruct` |
| Qwen | Qwen3 Coder 480B A35B Instruct | `Qwen/Qwen3-Coder-480B-A35B-Instruct` |
| Qwen | Qwen3 VL 8B Instruct | `Qwen/Qwen3-VL-8B-Instruct` |
| Qwen | Qwen3 VL 32B Instruct | `Qwen/Qwen3-VL-32B-Instruct` |
| Qwen | Qwen3 VL 30B A3B Instruct | `Qwen/Qwen3-VL-30B-A3B-Instruct` |
| Qwen | Qwen3 VL 235B A22B Instruct | `Qwen/Qwen3-VL-235B-A22B-Instruct` |
| Qwen | Qwen2.5 72B Instruct | `Qwen/Qwen2.5-72B-Instruct` |
| Qwen | Qwen2.5 72B | `Qwen/Qwen2.5-72B` |
| Qwen | Qwen2.5 32B Instruct | `Qwen/Qwen2.5-32B-Instruct` |
| Qwen | Qwen2.5 32B | `Qwen/Qwen2.5-32B` |
| Qwen | Qwen2.5 14B Instruct | `Qwen/Qwen2.5-14B-Instruct` |
| Qwen | Qwen2.5 14B | `Qwen/Qwen2.5-14B` |
| Qwen | Qwen2.5 7B Instruct | `Qwen/Qwen2.5-7B-Instruct` |
| Qwen | Qwen2.5 7B | `Qwen/Qwen2.5-7B` |
| Qwen | Qwen2.5 3B Instruct | `Qwen/Qwen2.5-3B-Instruct` |
| Qwen | Qwen2.5 3B | `Qwen/Qwen2.5-3B` |
| Qwen | Qwen2.5 1.5B Instruct | `Qwen/Qwen2.5-1.5B-Instruct` |
| Qwen | Qwen2.5 1.5B | `Qwen/Qwen2.5-1.5B` |
| Moonshot AI | Kimi K2.7 Code | `moonshotai/Kimi-K2.7-Code` |
| Moonshot AI | Kimi K2.6 | `moonshotai/Kimi-K2.6` |
| Moonshot AI | Kimi K2.5 | `moonshotai/Kimi-K2.5` |
| Moonshot AI | Kimi K2 Thinking | `moonshotai/Kimi-K2-Thinking` |
| Moonshot AI | Kimi K2 Instruct 0905 | `moonshotai/Kimi-K2-Instruct-0905` |
| Moonshot AI | Kimi K2 Instruct | `moonshotai/Kimi-K2-Instruct` |
| Moonshot AI | Kimi K2 Base | `moonshotai/Kimi-K2-Base` |
| 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` |
| Meta | Llama 4 Scout 17B 16E Instruct | `meta-llama/Llama-4-Scout-17B-16E-Instruct` |
| Meta | Llama 4 Scout 17B 16E Instruct VLM | `meta-llama/Llama-4-Scout-17B-16E-Instruct-VLM` |
| Meta | Llama 4 Maverick 17B 128E Instruct | `meta-llama/Llama-4-Maverick-17B-128E-Instruct` |
| Meta | Llama 4 Maverick 17B 128E Instruct VLM | `meta-llama/Llama-4-Maverick-17B-128E-Instruct-VLM` |
| Meta | Llama 3.3 70B Instruct Reference | `meta-llama/Llama-3.3-70B-Instruct-Reference` |
| Meta | Llama 3.3 70B 32k Instruct Reference | `meta-llama/Llama-3.3-70B-32k-Instruct-Reference` |
| Meta | Llama 3.3 70B 131k Instruct Reference | `meta-llama/Llama-3.3-70B-131k-Instruct-Reference` |
| Meta | Llama 3.2 3B Instruct | `meta-llama/Llama-3.2-3B-Instruct` |
| Meta | Llama 3.2 1B Instruct | `meta-llama/Llama-3.2-1B-Instruct` |
| Meta | Meta Llama 3.1 8B Instruct Reference | `meta-llama/Meta-Llama-3.1-8B-Instruct-Reference` |
| Meta | Meta Llama 3.1 8B 131k Instruct Reference | `meta-llama/Meta-Llama-3.1-8B-131k-Instruct-Reference` |
| Meta | Meta Llama 3.1 70B Instruct Reference | `meta-llama/Meta-Llama-3.1-70B-Instruct-Reference` |
| Meta | Meta Llama 3.1 70B 32k Instruct Reference | `meta-llama/Meta-Llama-3.1-70B-32k-Instruct-Reference` |
| Meta | Meta Llama 3.1 70B 131k Instruct Reference | `meta-llama/Meta-Llama-3.1-70B-131k-Instruct-Reference` |
| 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 line should carry:
* `messages`: The conversation. Assistant messages can include `tool_calls` (a list of structured invocation objects) in place of `content`. Tool results come back via messages with the `tool` role.
* `tools`: A list of available tools for the example.
### Conversational format
```json theme={null}
{
"messages": [
{"role": "system", "content": "You are a helpful travel planning assistant."},
{"role": "user", "content": "What is the current temperature in San Francisco?"},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_abc123",
"type": "function",
"function": {
"name": "getCurrentWeather",
"arguments": "{\"location\": \"San Francisco, CA\"}"
}
}
]
},
{"role": "tool", "content": "{\"location\": \"San Francisco\", \"temperature\": \"65\", \"unit\": \"fahrenheit\"}"}
],
"tools": [
{
"type": "function",
"function": {
"name": "getCurrentWeather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA."}
},
"required": ["location"]
}
}
}
]
}
```
### Preference format
For preference fine-tuning, the `tools` array nests inside `input`. See [Preference tuning](/docs/fine-tuning/preference-tuning) for the broader DPO workflow.
```json theme={null}
{
"input": {
"messages": [
{"role": "system", "content": "You are a helpful travel planning assistant."},
{"role": "user", "content": "What is the current temperature in San Francisco?"}
],
"tools": [
{"type": "function", "function": {
"name": "getCurrentWeather",
"description": "Get the current weather in a given location",
"parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}
}}
]
},
"preferred_output": [
{"role": "assistant", "tool_calls": [
{"id": "call_abc123", "type": "function", "function": {
"name": "getCurrentWeather", "arguments": "{\"location\": \"San Francisco, CA\"}"
}}
]}
],
"non_preferred_output": [
{"role": "assistant", "content": "Sorry, I can't help you with that."}
]
}
```
## 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="function_calling_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("function_calling_dataset.jsonl"),
purpose: "fine-tune",
});
console.log(trainFile.id);
```
```bash CLI theme={null}
tg files check "function_calling_dataset.jsonl"
tg files upload "function_calling_dataset.jsonl"
```
## Launch the job
LoRA is the default and recommended training mode. 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 all available parameters, see the [API reference](/reference/cli/finetune).
## Watch and deploy
Function-calling 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) and call it with the same [function-calling request shape](/docs/inference/function-calling/overview) as the base model.