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Add LFM2.5-230M to the LFM2.5 cookbook (#29321)

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Yi Zhong
2026-06-26 08:27:57 +08:00
committed by GitHub
parent 4b0b0ea55f
commit f83cbc2516
4 changed files with 86 additions and 4 deletions
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docs_new/cookbook/autoregressive/LiquidAI/LFM2.5.mdx
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@@ -156,6 +156,12 @@ This minimal hybrid layout was selected by a hardware-in-the-loop architecture s
<td style={{padding: "9px 12px", textAlign: "right", backgroundColor: "rgba(255,255,255,0.02)"}}>32K</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Compact instruct, structured output</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong><a href="https://huggingface.co/LiquidAI/LFM2.5-230M">LFM2.5-230M</a></strong></td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>230M (dense)</td>
<td style={{padding: "9px 12px", textAlign: "right", backgroundColor: "rgba(255,255,255,0.02)"}}>32K</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Most compact; data extraction, structured output</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong><a href="https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606">LFM2.5-1.2B-JP-202606</a></strong></td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1.17B (dense)</td>
@@ -189,7 +195,7 @@ This minimal hybrid layout was selected by a hardware-in-the-loop architecture s
</tbody>
</table>
The Deploy panel above covers the seven serving variants; **LFM2.5-1.2B-JP** (original — launch without `--tool-call-parser`) and the **Base** repos (pre-trained only, no post-training — see [§3.5](#35-base-checkpoints)) launch the same way with the model path swapped.
The Deploy panel above covers the eight serving variants; **LFM2.5-1.2B-JP** (original — launch without `--tool-call-parser`) and the **Base** repos (pre-trained only, no post-training — see [§3.5](#35-base-checkpoints)) launch the same way with the model path swapped.
**Choosing a variant:**
@@ -197,6 +203,7 @@ The Deploy panel above covers the seven serving variants; **LFM2.5-1.2B-JP** (or
- **1.2B-Thinking** — reasoning-heavy tasks: math, tool use, programming.
- **1.2B-Instruct** — the recommended pick for chat and creative writing.
- **350M** — tool use, data extraction, and structured output; not recommended for math, code, or creative writing.
- **230M** — the most compact checkpoint; same use as the 350M, not for math, code, or creative writing.
**License:** [LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/blob/main/LICENSE).
@@ -228,6 +235,7 @@ The Deploy panel above covers the seven serving variants; **LFM2.5-1.2B-JP** (or
<tr><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>LFM2.5-1.2B-Instruct</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.1</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>{`{"top_k": 50, "repetition_penalty": 1.05}`}</code></td></tr>
<tr><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>LFM2.5-1.2B-Thinking</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.05</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>{`{"top_k": 50, "repetition_penalty": 1.05}`}</code></td></tr>
<tr><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>LFM2.5-350M</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.1</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>{`{"top_k": 50, "repetition_penalty": 1.05}`}</code></td></tr>
<tr><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>LFM2.5-230M</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.1</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>{`{"top_k": 50, "repetition_penalty": 1.05}`}</code></td></tr>
<tr><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>LFM2.5-1.2B-JP-202606</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.1</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>{`{"top_k": 50, "repetition_penalty": 1.05}`}</code></td></tr>
<tr><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>LFM2.5-1.2B-JP</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.3</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>{`{"min_p": 0.15, "repetition_penalty": 1.05}`}</code></td></tr>
<tr><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>LFM2.5-VL-1.6B (text)</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.1</td><td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>{`{"min_p": 0.15, "repetition_penalty": 1.05}`}</code></td></tr>
@@ -253,6 +261,7 @@ SAMPLING = {
"LiquidAI/LFM2.5-1.2B-Instruct": dict(temperature=0.1, extra_body={"top_k": 50, "repetition_penalty": 1.05}),
"LiquidAI/LFM2.5-1.2B-Thinking": dict(temperature=0.05, extra_body={"top_k": 50, "repetition_penalty": 1.05}),
"LiquidAI/LFM2.5-350M": dict(temperature=0.1, extra_body={"top_k": 50, "repetition_penalty": 1.05}),
"LiquidAI/LFM2.5-230M": dict(temperature=0.1, extra_body={"top_k": 50, "repetition_penalty": 1.05}),
"LiquidAI/LFM2.5-1.2B-JP-202606": dict(temperature=0.1, extra_body={"top_k": 50, "repetition_penalty": 1.05}),
"LiquidAI/LFM2.5-VL-1.6B": dict(temperature=0.1, extra_body={"min_p": 0.15, "repetition_penalty": 1.05}),
"LiquidAI/LFM2.5-VL-450M": dict(temperature=0.1, extra_body={"min_p": 0.15, "repetition_penalty": 1.05}),
@@ -312,7 +321,7 @@ for call in resp.choices[0].message.tool_calls or []:
print(call.function.name, call.function.arguments)
```
Tool calling is supported on 8B-A1B, 1.2B-Thinking, 1.2B-Instruct, 350M, 1.2B-JP-202606, VL-1.6B, and VL-450M. For the **VL** models it is text-turn-only — do not combine an image and tools in the same turn.
Tool calling is supported on 8B-A1B, 1.2B-Thinking, 1.2B-Instruct, 350M, 230M, 1.2B-JP-202606, VL-1.6B, and VL-450M. For the **VL** models it is text-turn-only — do not combine an image and tools in the same turn.
### 3.4 Vision Input
@@ -335,7 +344,7 @@ print(resp.choices[0].message.content)
### 3.5 Base Checkpoints
Each size ships a pre-trained Base repo — [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base), [LFM2.5-350M-Base](https://huggingface.co/LiquidAI/LFM2.5-350M-Base), and [LFM2.5-8B-A1B-Base](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base) — intended for fine-tuning and continued pre-training.
Each size ships a pre-trained Base repo — [LFM2.5-230M-Base](https://huggingface.co/LiquidAI/LFM2.5-230M-Base), [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base), [LFM2.5-350M-Base](https://huggingface.co/LiquidAI/LFM2.5-350M-Base), and [LFM2.5-8B-A1B-Base](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base) — intended for fine-tuning and continued pre-training.
The repos ship a ChatML-style chat template, so `chat.completions` requests format normally. The checkpoints have no post-training, though — don't expect instruction following. For raw text continuation: