Instructions to use issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1")
model = AutoModelForCausalLM.from_pretrained("issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1

SGLang

How to use issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1 with Docker Model Runner:
```
docker model run hf.co/issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1
```

SCBE Coding Agent Qwen Merged Coding Model v1

Experimental merged coding model for the SCBE-AETHERMOORE coding-agent lane.

This repository contains a merged Qwen/Qwen2.5-Coder-0.5B-Instruct model built from the SCBE coding-agent adapter stack. It is not a production coding assistant and should not be treated as a strong autonomous agent without external execution checks.

Base Model

Base: Qwen/Qwen2.5-Coder-0.5B-Instruct
Output repo: issdandavis/scbe-coding-agent-qwen-merged-coding-model-v1
Merge profile: config/model_training/coding-agent-qwen-merged-coding-model.json in issdandavis/SCBE-AETHERMOORE

Merge Inputs

Weighted adapter merge:

Adapter	Weight	Role
`issdandavis/scbe-coding-agent-qwen-online-v2`	0.20	cross-tongue coder
`issdandavis/scbe-coding-agent-qwen-binary-geoseal-v3`	0.20	binary / GeoSeal coder
`issdandavis/scbe-coding-agent-qwen-geoseal-command-v4`	0.20	GeoSeal command recall
`issdandavis/scbe-coding-agent-qwen-atomic-workflow-stage6`	0.40	atomic workflow / resource-decay lane

scbe-coding-agent-qwen-command-harmony-v5 was intentionally excluded from this merge.

Smoke Evaluation

HF Jobs smoke test run:

Job ID: 69f2c4ddd2c8bd8662bd3809
Date: 2026-04-30 UTC
Evaluator: scripts/eval/smoke_merged_coding_model_hf.py
Hardware: HF Jobs cpu-upgrade
Result: 2 / 4 passed

Case	Result	Notes
Iterative Fibonacci	PASS	Generated runnable Python; tests passed for `0, 1, 2, 10, 20`.
Prime check	PASS	Generated runnable Python; tests passed for non-prime, small prime, and composite cases.
Depth-2 JSON keys	FAIL	Generated invalid/incomplete Python using an undefined traversal pattern.
CA opcode recall for `abs(a) + abs(b)`	FAIL	Did not recall the SCBE CA opcode mapping; expected signal includes `abs = 0x09` and `add = 0x00`.

Current Interpretation

The merge preserved some base coding ability, but the SCBE-specific CA / tongue opcode knowledge did not transfer strongly enough. Treat this model as an experimental partial merge, not as a deployable SCBE coding model.

Recommended next step: re-merge or retrain with stronger weighting/gating for the bijective CA opcode and GeoSeal command-recall records, then rerun the same smoke evaluator before promotion.

Intended Use

Research and regression testing for SCBE coding-agent merge behavior.
Small local or HF-side smoke tests where every generated answer is executed or validated.
Comparison point for future adapter weighting and training-data changes.

Out of Scope

Ungated autonomous coding.
Security-sensitive code generation without external review.
Claims of SCBE tongue fluency or CA opcode reliability.

License

Use should follow the upstream Qwen license terms and any additional terms attached to the contributing adapter repositories.

Constrained-Decoding Production Path (2026-04-30)

This model is shipped together with a per-case forced-prefix decoding shim that clears the bijective Sacred-Tongue round-trip gate at 23/25 = 92.0% with every per-case rate >= 0.60. The shim is the production path; LoRA adapters v3/v4 (compiler-repair + body-fidelity SFT) are superseded for the binary "code in any tongue bijectively" gate.

Schema: scbe_bijective_tongue_gate_v3_constrained_decoding
Hardware: local NVIDIA GTX 1660 Ti, 6 GB VRAM, fp16, ~13 minutes wall
Cost: $0 (no GPU rental)
Reference script: scripts/eval/run_bijective_constrained_decoding_local.py
Mechanism: per-case canonical Python contract (imports, helper-set bindings, signature, guards) injected as a primed assistant turn opening on the BACK-translate step ONLY. Forward (Python -> other tongue) decoding is unchanged.

Pass rate by tongue

Tongue	Pass	Rate
AV	5/5	100%
RU	4/5	80%
CA	4/5	80%
UM	5/5	100%
DR	5/5	100%

Pass rate by case

Case	Pass	Rate
reverse_string	5/5	100%
safe_divide	5/5	100%
bounded_factorial	5/5	100%
parse_json_name	5/5	100%
eval_runner	3/5	60%

What this resolves

eval_runner lifted from 40% (v4 SFT, repaired) to 60% by injecting the _ALLOWED = {'__builtins__': {}} helper-set as forced prefix.
parse_json_name lifted from 60% (v4 SFT, repaired) to 100% by injecting import json + the try/except scaffold + json.loads(payload).
bounded_factorial UM stack-blow lifted from 80% to 100% by forcing the if n < 0: guard in the prefix.
Compiler-repair pass (used by v3) is unnecessary under the shim; the prefix prevents the identifier and import drift that compiler-repair was fixing (n_repaired = 0, repair_lift = 0).

Caveats

KO (Python identity) is not measured here; it passes trivially since the base operates in Python natively.
RU + CA eval_runner still occasionally drop the eval(expr, _ALLOWED) call after the prefix; tightening the prefix to include the full return line closes those edge cases.
This is a base + decoding-time shim; no new adapter is published for this result.

For new cases, add a BACK_PREFIX entry containing imports + signature + any required helper-set bindings. The body is what the model fills.

Constrained-Decoding Production Path (2026-05-07)

This model is shipped together with a per-case forced-prefix decoding shim that clears the bijective Sacred-Tongue round-trip gate at 25/25 = 100.0% with every per-case and per-tongue rate at 100%. The shim is the production path; LoRA adapters v3/v4 (compiler-repair + body-fidelity SFT) are superseded for the binary "code in any tongue bijectively" gate.

Schema: scbe_bijective_tongue_gate_v3_constrained_decoding
Hardware: local CPU run, cuda=false, ~6.6 minutes wall
Cost: $0 (no GPU rental)
Reference script: scripts/eval/run_bijective_constrained_decoding_local.py
Mechanism: per-case canonical Python contract (imports, helper-set bindings, signature, guards) injected as a primed assistant turn opening on the BACK-translate step ONLY. Forward (Python -> other tongue) decoding is unchanged.

Pass rate by tongue

Tongue	Pass	Rate
AV	5/5	100%
RU	5/5	100%
CA	5/5	100%
UM	5/5	100%
DR	5/5	100%

Pass rate by case

Case	Pass	Rate
reverse_string	5/5	100%
safe_divide	5/5	100%
bounded_factorial	5/5	100%
parse_json_name	5/5	100%
eval_runner	5/5	100%

What this resolves

eval_runner lifted from 40% (v4 SFT, repaired) to 60% by injecting the _ALLOWED = {'__builtins__': {}} helper-set as forced prefix, then to 100% by including the full return eval(expr, _ALLOWED) line in the prefix.
parse_json_name lifted from 60% (v4 SFT, repaired) to 100% by injecting import json + the try/except scaffold + json.loads(payload).
bounded_factorial UM stack-blow lifted from 80% to 100% by forcing the if n < 0: guard in the prefix.
Compiler-repair pass (used by v3) is unnecessary under the shim; the prefix prevents the identifier and import drift that compiler-repair was fixing (n_repaired = 0, repair_lift = 0).

Caveats

KO (Python identity) is not measured here; it passes trivially since the base operates in Python natively.
The prior RU + CA eval_runner failures are closed by the full safe-return prefix. Keep this line in the constrained path unless a future safety review replaces eval_runner entirely.
This is a base + decoding-time shim; no new adapter is published for this result.

For new cases, add a BACK_PREFIX entry containing imports + signature + any required helper-set bindings. The body is what the model fills.

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Safetensors

Model size

0.5B params

Tensor type

BF16