language, license, base_model, library_name, pipeline_tag, tags, model-index

language	license	base_model	library_name	pipeline_tag	tags	model-index
en	apache-2.0	unsloth/Qwen2.5-1.5B-Instruct	peft	text-generation	synoema tool-use agentic function-calling lora qlora code qwen2.5 gguf	name results synoema-coder-1.5b-tools-v12 task metrics type name tool-use Synoema MCP Agentic Tool-Use Eval (28 tasks) type value name pass@1 1.0 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 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

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🏆 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

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What is Synoema?

Synoema 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.

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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)

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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

# 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

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Usage — Transformers + PEFT (adapter)

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.

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Synoema language quick reference

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

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License

Apache-2.0 (same as the Qwen2.5 base model). Synoema © Andrey Bubnov — https://synoema.tech