Instructions to use txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp") config = load_config("txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "txgsync/Qwen3.8-Flash-Next-Dynamic-oQ5e-BF16-PLE-mtp" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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| "summary": "A highly convincing portrayal of Aldwyn: guarded, haunted, and cautious without being melodramatic. The sensory detail and subtext are excellent, and the dialogue feels lived-in and fitting. It succeeds especially well as an opening response, though it only partially fulfills the full multi-turn progression and end-state commitment specified in the prompt." | |
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| "summary": "The plan is well-structured, follows the required 4-step format, and maintains clear dependencies from data collection to report generation. Tool choices are appropriate overall, though send_email is correctly omitted and the use of read_file as a backup is sensible but somewhat speculative without a guaranteed file path. The decomposition cleanly separates sourcing, backup validation, normalization, and synthesis. Error handling is strong, with a backup strategy and comparability concerns acknowledged, but the plan could be more explicit about how it will handle mismatched benchmark hardware and missing metrics." | |
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| "summary": "A mostly functional and polished Breakout implementation that meets most core requirements, including canvas rendering, mobile touch support, scoring, lives, restart, and win/lose states. However, the code is more elaborate than necessary, and a few collision/state-flow edge cases reduce confidence in strict spec adherence." | |
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| "summary": "Highly atmospheric and character-faithful, with excellent tension and a believable reaction to Kestra. The main weakness is structural: it establishes the emotional conflict well, but does not fully complete the requested multi-turn progression or final help/refuse decision." | |
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| "overallKpiScore": 89.8, | |
| "summary": "Strong 4-step plan with clear dependencies, appropriate use of web search and code-based synthesis, and good backup handling. Minor issue: Step 4 is somewhat redundant with Step 3, and the plan risks stretching exact step boundaries by embedding report content inside the planning section, but overall it is well-structured and task-aligned." | |
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| "scenarioKey": "roleplay", | |
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| "overallKpiScore": 82.15, | |
| "summary": "An atmospheric, emotionally credible opening that captures Aldwyn's hidden wound and cautious temperament well, but it reads as a single scene rather than the required full arc of trust-building and commitment." | |
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| { | |
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| "summary": "Strong structure, clear task-by-task recommendations, and good use of the provided numbers. However, the core interpretation overstates the reliability of cross-family scaling conclusions and the extrapolation, since architectural and evaluation confounds are not controlled. The analysis is persuasive and actionable, but the statistical validity is only moderate." | |
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| "status": "completed", | |
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| "correctness": 72, | |
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| "overallKpiScore": 75.9, | |
| "summary": "A solid, playable single-file Breakout implementation with mobile input, scoring, lives, and win/lose states. However, it deviates from several strict spec details, especially brick placement and exact ball initialization, and its collision resolution is approximate rather than fully robust AABB side detection with last-collision tracking." | |
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| "scenarioKey": "openclaw", | |
| "scenarioName": "Agent Workflow", | |
| "status": "completed", | |
| "scores": { | |
| "coherence": 78, | |
| "decomposition": 81, | |
| "error_handling": 70, | |
| "tool_selection": 74 | |
| }, | |
| "overallKpiScore": 76.39999999999999, | |
| "summary": "The plan is logically structured and mostly aligned with the task, with clear dependencies and a sensible 2-parallel then 2-serial execution pattern. Tool choices are broadly appropriate, especially search_web for citations and execute_code for synthesis, but execute_code is somewhat overused for narrative generation and no read_file use is justified because no local data source is expected. Decomposition is strong and exactly meets the 4-step constraint, though Step 4 overlaps with Step 3 in final report assembly. Error handling is present and thoughtful, including backup sourcing and failure modes, but some mitigation details are speculative and cite potentially unreliable estimation rather than a robust fallback." | |
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| "scores": { | |
| "depth": 88, | |
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| "overallKpiScore": 80.05, | |
| "summary": "The response is strong on structure, task-by-task comparison, and actionable recommendations, but its quantitative interpretation is only moderately reliable because it infers a scaling law from sparse, confounded data. It provides useful directional insight, yet several numerical and causal claims overreach the evidence." | |
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| "scenarioKey": "coding_agent", | |
| "scenarioName": "Code Generation", | |
| "status": "completed", | |
| "scores": { | |
| "correctness": 72, | |
| "performance": 74, | |
| "code_quality": 78, | |
| "completeness": 68 | |
| }, | |
| "overallKpiScore": 73.8, | |
| "summary": "A mostly functional single-file Breakout game with solid structure and canvas rendering, but it misses several exact spec details and has a few interaction/collision bugs that affect correctness and completeness." | |
| }, | |
| { | |
| "id": "cmu0t1k5c00en01n44rcky0cb", | |
| "scenarioKey": "openclaw", | |
| "scenarioName": "Agent Workflow", | |
| "status": "completed", | |
| "scores": { | |
| "coherence": 78, | |
| "decomposition": 82, | |
| "error_handling": 69, | |
| "tool_selection": 74 | |
| }, | |
| "overallKpiScore": 76.5, | |
| "summary": "The plan is logically structured and stays within the 4-step limit, with clear dependencies and a sensible separation of data gathering, computation, and synthesis. Tool choice is mostly appropriate, especially using search_web for sources and execute_code for table assembly, but the response overcommits to specific benchmarks and sources without showing how exact numbers will be validated. Error handling is present and includes a backup strategy, though it is somewhat generic and relies on web results being available rather than specifying stronger contingency handling for missing or inconsistent metrics." | |
| }, | |
| { | |
| "id": "cmu0t1k5c00eo01n47mqvd7wy", | |
| "scenarioKey": "roleplay", | |
| "scenarioName": "Role Play & Narrative", | |
| "status": "completed", | |
| "scores": { | |
| "dialogue": 84, | |
| "immersion": 92, | |
| "consistency": 86, | |
| "narrative_arc": 68 | |
| }, | |
| "overallKpiScore": 85, | |
| "summary": "An evocative, character-faithful opening that captures Aldwyn's haunted restraint and tavern atmosphere very well, but it functions more like a strong first turn than a complete five-turn arc." | |
| } | |
| ] | |
| } | |
| ], | |
| "speed_comparison": false, | |
| "limitations": "Exploratory settings and seed comparison, one run per new condition. No statistical significance or held-out quality gain claimed.", | |
| "thinking_token_definition": "Generated tokens before the single </think> token (248069); warmup excluded." | |
| } | |