Instructions to use Aliguinga01/rule_violation2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Aliguinga01/rule_violation2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./llama-cli -hf Aliguinga01/rule_violation2:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Aliguinga01/rule_violation2:F16
Use Docker
docker model run hf.co/Aliguinga01/rule_violation2:F16
- LM Studio
- Jan
- Ollama
How to use Aliguinga01/rule_violation2 with Ollama:
ollama run hf.co/Aliguinga01/rule_violation2:F16
- Unsloth Studio
How to use Aliguinga01/rule_violation2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Aliguinga01/rule_violation2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Aliguinga01/rule_violation2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Aliguinga01/rule_violation2 to start chatting
- Docker Model Runner
How to use Aliguinga01/rule_violation2 with Docker Model Runner:
docker model run hf.co/Aliguinga01/rule_violation2:F16
- Lemonade
How to use Aliguinga01/rule_violation2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Aliguinga01/rule_violation2:F16
Run and chat with the model
lemonade run user.rule_violation2-F16
List all available models
lemonade list
- Atomic Chat
| // Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers. | |
| struct common_chat_templates; | |
| struct common_chat_tool_call { | |
| std::string name; | |
| std::string arguments; | |
| std::string id; | |
| bool operator==(const common_chat_tool_call & other) const { | |
| return name == other.name && arguments == other.arguments && id == other.id; | |
| } | |
| }; | |
| struct common_chat_msg_content_part { | |
| std::string type; | |
| std::string text; | |
| bool operator==(const common_chat_msg_content_part & other) const { | |
| return type == other.type && text == other.text; | |
| } | |
| }; | |
| struct common_chat_msg { | |
| std::string role; | |
| std::string content; | |
| std::vector<common_chat_msg_content_part> content_parts; | |
| std::vector<common_chat_tool_call> tool_calls; | |
| std::string reasoning_content; | |
| std::string tool_name; | |
| std::string tool_call_id; | |
| template <class T> T to_json_oaicompat() const; | |
| bool empty() const { | |
| return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && tool_name.empty() && tool_call_id.empty(); | |
| } | |
| void set_tool_call_ids(std::vector<std::string> & ids_cache, const std::function<std::string()> & gen_tool_call_id) { | |
| for (auto i = 0u; i < tool_calls.size(); i++) { | |
| if (ids_cache.size() <= i) { | |
| auto id = tool_calls[i].id; | |
| if (id.empty()) { | |
| id = gen_tool_call_id(); | |
| } | |
| ids_cache.push_back(id); | |
| } | |
| tool_calls[i].id = ids_cache[i]; | |
| } | |
| } | |
| bool operator==(const common_chat_msg & other) const { | |
| return role == other.role | |
| && content == other.content | |
| && content_parts == other.content_parts | |
| && tool_calls == other.tool_calls | |
| && reasoning_content == other.reasoning_content | |
| && tool_name == other.tool_name | |
| && tool_call_id == other.tool_call_id; | |
| } | |
| bool operator!=(const common_chat_msg & other) const { | |
| return !(*this == other); | |
| } | |
| }; | |
| struct common_chat_msg_diff { | |
| std::string reasoning_content_delta; | |
| std::string content_delta; | |
| size_t tool_call_index = std::string::npos; | |
| common_chat_tool_call tool_call_delta; | |
| static std::vector<common_chat_msg_diff> compute_diffs(const common_chat_msg & previous_msg, const common_chat_msg & new_msg); | |
| bool operator==(const common_chat_msg_diff & other) const { | |
| return content_delta == other.content_delta | |
| && tool_call_index == other.tool_call_index | |
| && tool_call_delta == other.tool_call_delta; | |
| } | |
| }; | |
| struct common_chat_tool { | |
| std::string name; | |
| std::string description; | |
| std::string parameters; | |
| }; | |
| enum common_chat_tool_choice { | |
| COMMON_CHAT_TOOL_CHOICE_AUTO, | |
| COMMON_CHAT_TOOL_CHOICE_REQUIRED, | |
| COMMON_CHAT_TOOL_CHOICE_NONE, | |
| }; | |
| enum common_chat_format { | |
| COMMON_CHAT_FORMAT_CONTENT_ONLY, | |
| COMMON_CHAT_FORMAT_GENERIC, | |
| COMMON_CHAT_FORMAT_MISTRAL_NEMO, | |
| COMMON_CHAT_FORMAT_MAGISTRAL, | |
| COMMON_CHAT_FORMAT_LLAMA_3_X, | |
| COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS, | |
| COMMON_CHAT_FORMAT_DEEPSEEK_R1, | |
| COMMON_CHAT_FORMAT_FIREFUNCTION_V2, | |
| COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, | |
| COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1, | |
| COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, | |
| COMMON_CHAT_FORMAT_HERMES_2_PRO, | |
| COMMON_CHAT_FORMAT_COMMAND_R7B, | |
| COMMON_CHAT_FORMAT_GRANITE, | |
| COMMON_CHAT_FORMAT_GPT_OSS, | |
| COMMON_CHAT_FORMAT_SEED_OSS, | |
| COMMON_CHAT_FORMAT_NEMOTRON_V2, | |
| COMMON_CHAT_FORMAT_APERTUS, | |
| COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats | |
| }; | |
| struct common_chat_templates_inputs { | |
| std::vector<common_chat_msg> messages; | |
| std::string grammar; | |
| std::string json_schema; | |
| bool add_generation_prompt = true; | |
| bool use_jinja = true; | |
| // Parameters below only supported when use_jinja is true | |
| std::vector<common_chat_tool> tools; | |
| common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO; | |
| bool parallel_tool_calls = false; | |
| common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; | |
| bool enable_thinking = true; | |
| std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); | |
| std::map<std::string, std::string> chat_template_kwargs; | |
| bool add_bos = false; | |
| bool add_eos = false; | |
| }; | |
| struct common_chat_params { | |
| common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; | |
| std::string prompt; | |
| std::string grammar; | |
| bool grammar_lazy = false; | |
| bool thinking_forced_open = false; | |
| std::vector<common_grammar_trigger> grammar_triggers; | |
| std::vector<std::string> preserved_tokens; | |
| std::vector<std::string> additional_stops; | |
| }; | |
| struct common_chat_syntax { | |
| common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; | |
| common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; | |
| // Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode) | |
| bool reasoning_in_content = false; | |
| bool thinking_forced_open = false; | |
| bool parse_tool_calls = true; | |
| }; | |
| // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid | |
| bool common_chat_verify_template(const std::string & tmpl, bool use_jinja); | |
| void common_chat_templates_free(struct common_chat_templates * tmpls); | |
| struct common_chat_templates_deleter { void operator()(common_chat_templates * tmpls) { common_chat_templates_free(tmpls); } }; | |
| typedef std::unique_ptr<struct common_chat_templates, common_chat_templates_deleter> common_chat_templates_ptr; | |
| common_chat_templates_ptr common_chat_templates_init( | |
| const struct llama_model * model, | |
| const std::string & chat_template_override, | |
| const std::string & bos_token_override = "", | |
| const std::string & eos_token_override = ""); | |
| bool common_chat_templates_was_explicit(const struct common_chat_templates * tmpls); | |
| const char * common_chat_templates_source(const struct common_chat_templates * tmpls, const char * variant = nullptr); | |
| struct common_chat_params common_chat_templates_apply( | |
| const struct common_chat_templates * tmpls, | |
| const struct common_chat_templates_inputs & inputs); | |
| // Format single message, while taking into account the position of that message in chat history | |
| std::string common_chat_format_single( | |
| const struct common_chat_templates * tmpls, | |
| const std::vector<common_chat_msg> & past_msg, | |
| const common_chat_msg & new_msg, | |
| bool add_ass, | |
| bool use_jinja); | |
| // Returns an example of formatted chat | |
| std::string common_chat_format_example( | |
| const struct common_chat_templates * tmpls, | |
| bool use_jinja, | |
| const std::map<std::string, std::string> & chat_template_kwargs); | |
| const char* common_chat_format_name(common_chat_format format); | |
| const char* common_reasoning_format_name(common_reasoning_format format); | |
| common_reasoning_format common_reasoning_format_from_name(const std::string & format); | |
| common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax); | |
| common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice); | |
| bool common_chat_templates_support_enable_thinking(const common_chat_templates * chat_templates); | |
| // Parses a JSON array of messages in OpenAI's chat completion API format. | |
| // T can be std::string containing JSON or nlohmann::ordered_json | |
| template <class T> std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const T & messages); | |
| template <class T> T common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false); | |
| // Parses a JSON array of tools in OpenAI's chat completion tool call API format. | |
| // T can be std::string containing JSON or nlohmann::ordered_json | |
| template <class T> std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const T & tools); | |
| template <class T> T common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools); | |
| template <class T> T common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff); | |