140 lines
5.2 KiB
Markdown
140 lines
5.2 KiB
Markdown
---
|
||
language: [en]
|
||
license: apache-2.0
|
||
base_model: unsloth/Qwen2.5-1.5B-Instruct
|
||
library_name: peft
|
||
pipeline_tag: text-generation
|
||
tags: [synoema, tool-use, agentic, function-calling, lora, qlora, code, qwen2.5, gguf]
|
||
model-index:
|
||
- name: synoema-coder-1.5b-tools-v12
|
||
results:
|
||
- task: {type: tool-use, name: Synoema MCP Agentic Tool-Use Eval (28 tasks)}
|
||
metrics:
|
||
- {type: pass@1, value: 1.0, name: "28-task agentic eval (28/28)"}
|
||
---
|
||
|
||
# Synoema-Coder-1.5B Tools (C12)
|
||
|
||
A **1.5B** LoRA fine-tune of `unsloth/Qwen2.5-1.5B-Instruct` that turns it into an **agentic coding model for the
|
||
[Synoema](https://synoema.tech) programming language** — it writes Synoema, type-checks it,
|
||
runs it, searches a corpus, and self-corrects on errors, all through MCP tools.
|
||
|
||
- 🌐 **Website:** https://synoema.tech
|
||
- 🤖 **This model:** https://huggingface.co/delimitter/synoema-coder-1.5b-tools-v12
|
||
- 📚 **Training corpus (dataset):** https://huggingface.co/datasets/delimitter/synoema-coder-3b-tools-corpus
|
||
|
||
---
|
||
|
||
## 🏆 Result: **100% (28/28)** on the Synoema agentic tool-use benchmark
|
||
|
||
Scored on the **corrected agentic harness**: the model is driven **turn-by-turn** (generation
|
||
stops at `<|im_end|>`), and **real** tool results are injected between turns — actual
|
||
`sno check` / `sno run` output from the live Synoema compiler, never mocked. A task only passes
|
||
if the model genuinely completes it end-to-end (e.g. multi-write self-correction: write broken
|
||
code → observe the type error → rewrite a valid fix → type-check passes).
|
||
|
||
| Capability | Tasks | Pass |
|
||
|---|---|---|
|
||
| Write + typecheck + run | TU1–TU3, TU5, TU10 | ✅ |
|
||
| Search → write → run | TU6, TU9, TU20 | ✅ |
|
||
| Multi-write self-correction (if/else → ternary) | TU4, TU13 | ✅ |
|
||
| Language features (ADT, HOF, pattern match, cons) | TU11, TU14–TU19, TU23, TU29 | ✅ |
|
||
| List comprehensions | TU12, TU26 | ✅ |
|
||
| Nested ternary (fizzbuzz) | TU22, TU30 | ✅ |
|
||
| **Total** | **28** | **28/28** |
|
||
|
||
---
|
||
|
||
## What is Synoema?
|
||
|
||
[Synoema](https://synoema.tech) is an **LLM-native programming language and runtime** designed so
|
||
that models can write it reliably:
|
||
|
||
- **BPE-aligned operators** — every operator maps to exactly one `cl100k_base` token.
|
||
- **Ternary instead of if/else** — `? cond -> a : b` (nestable).
|
||
- **GBNF grammar** for constrained decoding (structural-correctness guarantee).
|
||
- **Cranelift JIT + WebAssembly** compile targets.
|
||
- **MCP server** exposing `file_write`, `file_read`, `sno_typecheck`, `sno_run`, `search_corpus`.
|
||
- **Contract annotations** (`requires` / `ensures`) for formal verification.
|
||
|
||
---
|
||
|
||
## Model details
|
||
|
||
| Property | Value |
|
||
|---|---|
|
||
| Base model | `unsloth/Qwen2.5-1.5B-Instruct` |
|
||
| Parameters | 1.5B |
|
||
| Method | QLoRA (4-bit NF4 + LoRA), merged to fp16 for GGUF |
|
||
| LoRA | r=16, alpha=32 |
|
||
| Sequence length | 1024 |
|
||
| Epochs / cycle | 3 |
|
||
| Training corpus | ~18k tool-use + codegen examples — **every example passes `sno check` + `sno run`** |
|
||
| Cycle | C12 (sequential "carousel": each cycle warm-starts from the best previous adapter, then trains on the corpus plus targeted examples for the prior cycle's failures) |
|
||
| Hardware | AMD RX 7900 GRE 16GB (ROCm + unsloth) |
|
||
|
||
---
|
||
|
||
## GGUF files (llama.cpp / Ollama / LM Studio)
|
||
|
||
| File | Quant | Size | Notes |
|
||
|---|---|---|---|
|
||
| `synoema-coder-1.5b-tools-v12.Q4_K_M.gguf` | Q4_K_M | 940 MB | smallest, recommended for local use |
|
||
| `synoema-coder-1.5b-tools-v12.Q8_0.gguf` | Q8_0 | 2 GB | near-lossless |
|
||
| `synoema-coder-1.5b-tools-v12.f16.gguf` | F16 | 3 GB | full precision |
|
||
|
||
```bash
|
||
# llama.cpp
|
||
llama-cli -hf delimitter/synoema-coder-1.5b-tools-v12 --hf-file synoema-coder-1.5b-tools-v12.Q4_K_M.gguf -p "Write quicksort in Synoema to src/qs.sno and run it."
|
||
|
||
# Ollama
|
||
ollama run hf.co/delimitter/synoema-coder-1.5b-tools-v12:Q4_K_M
|
||
```
|
||
|
||
---
|
||
|
||
## Usage — Transformers + PEFT (adapter)
|
||
|
||
```python
|
||
from peft import PeftModel
|
||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
|
||
base = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct", device_map="auto")
|
||
tok = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct")
|
||
model = PeftModel.from_pretrained(base, "delimitter/synoema-coder-1.5b-tools-v12")
|
||
```
|
||
|
||
Prompt format is **ChatML**. The system prompt used at training/eval:
|
||
|
||
```
|
||
<|im_start|>system
|
||
You are sno-code, a Synoema coding agent. Use tools to write and verify code.<|im_end|>
|
||
<|im_start|>user
|
||
Write `square x = x * x` with `main = square 9` to src/square.sno, typecheck and run it.<|im_end|>
|
||
<|im_start|>assistant
|
||
```
|
||
|
||
The model emits OpenAI-style `tool_calls` for `file_write`, `sno_typecheck`, `sno_run`,
|
||
`file_read`, `search_corpus`; feed real tool results back as `tool` turns.
|
||
|
||
---
|
||
|
||
## Synoema language quick reference
|
||
|
||
```synoema
|
||
maxOf x y = ? x > y -> x : y -- ternary (NO if/then/else)
|
||
fact 0 = 1 -- pattern matching
|
||
fact n = n * fact (n - 1)
|
||
evens xs = [x | x <- xs, x % 2 == 0] -- list comprehension
|
||
sumList xs = foldl (\acc x -> acc + x) 0 xs -- higher-order functions
|
||
Direction = North | South | East | West -- ADT
|
||
opposite North = South
|
||
main = qsort [3 1 4 1 5] -- lists are SPACE-separated
|
||
```
|
||
|
||
---
|
||
|
||
## License
|
||
|
||
Apache-2.0 (same as the Qwen2.5 base model). **Synoema** © Andrey Bubnov — https://synoema.tech
|